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miazhao/deberta_base_model_s3_ccnet_airbnb_dat_continue3
283b0a2b23e5926f615b9eb058a7a177668e9adc
2022-06-04T14:24:23.000Z
[ "pytorch", "deberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
miazhao
null
miazhao/deberta_base_model_s3_ccnet_airbnb_dat_continue3
17
null
transformers
9,100
Entry not found
facebook/levit-192
b92b6fda489d116d82b1ae9d4e3b19620b319685
2022-06-01T13:20:39.000Z
[ "pytorch", "levit", "image-classification", "dataset:imagenet-1k", "arxiv:2104.01136", "transformers", "vision", "license:apache-2.0" ]
image-classification
false
facebook
null
facebook/levit-192
17
null
transformers
9,101
--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # LeViT LeViT-192 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference ](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT). Disclaimer: The team releasing LeViT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Usage Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import LevitFeatureExtractor, LevitForImageClassificationWithTeacher from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = LevitFeatureExtractor.from_pretrained('facebook/levit-192') model = LevitForImageClassificationWithTeacher.from_pretrained('facebook/levit-192') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ```
kktoto/tiny_bb_wd
6dbbfb6437c501ab5a7fba5d2798dc5ff4093156
2022-06-02T08:06:44.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
token-classification
false
kktoto
null
kktoto/tiny_bb_wd
17
null
transformers
9,102
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: tiny_bb_wd results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tiny_bb_wd This model is a fine-tuned version of [kktoto/tiny_bb_wd](https://huggingface.co/kktoto/tiny_bb_wd) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1331 - Precision: 0.6566 - Recall: 0.6502 - F1: 0.6533 - Accuracy: 0.9524 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1193 | 1.0 | 5561 | 0.1398 | 0.6406 | 0.6264 | 0.6335 | 0.9501 | | 0.1259 | 2.0 | 11122 | 0.1343 | 0.6476 | 0.6300 | 0.6387 | 0.9509 | | 0.1283 | 3.0 | 16683 | 0.1333 | 0.6484 | 0.6367 | 0.6425 | 0.9512 | | 0.1217 | 4.0 | 22244 | 0.1325 | 0.6524 | 0.6380 | 0.6451 | 0.9516 | | 0.12 | 5.0 | 27805 | 0.1337 | 0.6571 | 0.6377 | 0.6472 | 0.9522 | | 0.1187 | 6.0 | 33366 | 0.1319 | 0.6630 | 0.6297 | 0.6459 | 0.9525 | | 0.116 | 7.0 | 38927 | 0.1318 | 0.6600 | 0.6421 | 0.6509 | 0.9525 | | 0.1125 | 8.0 | 44488 | 0.1337 | 0.6563 | 0.6481 | 0.6522 | 0.9523 | | 0.1118 | 9.0 | 50049 | 0.1329 | 0.6575 | 0.6477 | 0.6526 | 0.9524 | | 0.1103 | 10.0 | 55610 | 0.1331 | 0.6566 | 0.6502 | 0.6533 | 0.9524 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
RUCAIBox/mvp-question-answering
4c2aabefbd0aa809745251eb51e6d7ddbc4efa40
2022-06-27T02:28:05.000Z
[ "pytorch", "mvp", "en", "arxiv:2206.12131", "transformers", "text-generation", "text2text-generation", "license:apache-2.0" ]
text2text-generation
false
RUCAIBox
null
RUCAIBox/mvp-question-answering
17
1
transformers
9,103
--- license: apache-2.0 language: - en tags: - text-generation - text2text-generation pipeline_tag: text2text-generation widget: - text: "Answer the following question: From which country did Angola achieve independence in 1975?" example_title: "Example1" - text: "Answer the following question: what is ce certified [X_SEP] The CE marking is the manufacturer's declaration that the product meets the requirements of the applicable EC directives. Officially, CE is an abbreviation of Conformite Conformité, europeenne Européenne Meaning. european conformity" example_title: "Example2" --- # MVP-question-answering The MVP-question-answering model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen. The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP). ## Model Description MVP-question-answering is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled question answering datasets. It is a variant (MVP+S) of our [MVP](https://huggingface.co/RUCAIBox/mvp) [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a Transformer encoder-decoder architecture with layer-wise prompts. MVP-question-answering is specially designed for question answering tasks, such as reading comprehension (SQuAD), conversational question answering (CoQA) and closed-book question-answering (Natural Questions). ## Example ```python >>> from transformers import MvpTokenizer, MvpForConditionalGeneration >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-question-answering") >>> inputs = tokenizer( ... "Answer the following question: From which country did Angola achieve independence in 1975?", ... return_tensors="pt", ... ) >>> generated_ids = model.generate(**inputs) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ['Portugal'] ``` ## Related Models **MVP**: [https://huggingface.co/RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp). **Prompt-based models**: - MVP-multi-task: [https://huggingface.co/RUCAIBox/mvp-multi-task](https://huggingface.co/RUCAIBox/mvp-multi-task). - MVP-summarization: [https://huggingface.co/RUCAIBox/mvp-summarization](https://huggingface.co/RUCAIBox/mvp-summarization). - MVP-open-dialog: [https://huggingface.co/RUCAIBox/mvp-open-dialog](https://huggingface.co/RUCAIBox/mvp-open-dialog). - MVP-data-to-text: [https://huggingface.co/RUCAIBox/mvp-data-to-text](https://huggingface.co/RUCAIBox/mvp-data-to-text). - MVP-story: [https://huggingface.co/RUCAIBox/mvp-story](https://huggingface.co/RUCAIBox/mvp-story). - MVP-question-answering: [https://huggingface.co/RUCAIBox/mvp-question-answering](https://huggingface.co/RUCAIBox/mvp-question-answering). - MVP-question-generation: [https://huggingface.co/RUCAIBox/mvp-question-generation](https://huggingface.co/RUCAIBox/mvp-question-generation). - MVP-task-dialog: [https://huggingface.co/RUCAIBox/mvp-task-dialog](https://huggingface.co/RUCAIBox/mvp-task-dialog). **Multi-task models**: - MTL-summarization: [https://huggingface.co/RUCAIBox/mtl-summarization](https://huggingface.co/RUCAIBox/mtl-summarization). - MTL-open-dialog: [https://huggingface.co/RUCAIBox/mtl-open-dialog](https://huggingface.co/RUCAIBox/mtl-open-dialog). - MTL-data-to-text: [https://huggingface.co/RUCAIBox/mtl-data-to-text](https://huggingface.co/RUCAIBox/mtl-data-to-text). - MTL-story: [https://huggingface.co/RUCAIBox/mtl-story](https://huggingface.co/RUCAIBox/mtl-story). - MTL-question-answering: [https://huggingface.co/RUCAIBox/mtl-question-answering](https://huggingface.co/RUCAIBox/mtl-question-answering). - MTL-question-generation: [https://huggingface.co/RUCAIBox/mtl-question-generation](https://huggingface.co/RUCAIBox/mtl-question-generation). - MTL-task-dialog: [https://huggingface.co/RUCAIBox/mtl-task-dialog](https://huggingface.co/RUCAIBox/mtl-task-dialog). ## Citation ```bibtex @article{tang2022mvp, title={MVP: Multi-task Supervised Pre-training for Natural Language Generation}, author={Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong}, journal={arXiv preprint arXiv:2206.12131}, year={2022}, url={https://arxiv.org/abs/2206.12131}, } ```
Massinissa/Jeux2BERT
2299c0c1231c56390358f5f52325af785982fc5e
2022-06-22T12:45:12.000Z
[ "pytorch", "flaubert", "feature-extraction", "transformers" ]
feature-extraction
false
Massinissa
null
Massinissa/Jeux2BERT
17
null
transformers
9,104
# Jeux2BERT Jeux2BERT is a Flaubert language model augmented by the lexico-semantic network JeuxDeMots. Thus, this model tries to capture the distributional and relational properties of words, but also tries to discriminate the different relational properties between words or syntagms. The Web application includes three Tasks : Link Prediction (Classification de triplets), Relation Prediction (Prédiction de Relation) and Triple Ranking (Classement de triplets) # Web App [https://github.com/atmani-massinissa/Jeux2BERT_APP/tree/main] # Demo [https://share.streamlit.io/atmani-massinissa/jeux2bert_app/main/app.py?page=Classement+de+triplets] The task Triple Ranking (Classement de triplets) don't run smoothly on the streamlit server because of the inference's time, so it's better to run it locally instead on the demo's server.
Yip/bert-finetuned-ner-chinese
51b99198350d84f099ef8962306269a0bc4f8bf6
2022-06-09T07:01:04.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
Yip
null
Yip/bert-finetuned-ner-chinese
17
null
transformers
9,105
Entry not found
ghadeermobasher/Original-SciBERT-BC5CDR-Disease
3886c9bf3c376e41df397f76d281c98fb54b1e77
2022-06-09T11:32:31.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
ghadeermobasher
null
ghadeermobasher/Original-SciBERT-BC5CDR-Disease
17
null
transformers
9,106
Entry not found
ghadeermobasher/Original-SciBERT-BC4CHEMD
6b851f7c3b26b9f9a05a2748a205652a29d291da
2022-06-09T19:35:59.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
ghadeermobasher
null
ghadeermobasher/Original-SciBERT-BC4CHEMD
17
null
transformers
9,107
Entry not found
ghadeermobasher/Original-SciBERT-BC5CDR-Chemical-T2
816382ae0281a692800e63a49a3df99fe5b5e042
2022-06-09T18:25:26.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
ghadeermobasher
null
ghadeermobasher/Original-SciBERT-BC5CDR-Chemical-T2
17
null
transformers
9,108
Entry not found
course5i/SEAD-L-6_H-384_A-12-mnli
9052bac862ae36c60025a64a7c888730d603376a
2022-06-12T22:59:51.000Z
[ "pytorch", "tf", "jax", "bert", "text-classification", "en", "dataset:glue", "dataset:mnli", "arxiv:1910.01108", "arxiv:1909.10351", "arxiv:2002.10957", "arxiv:1810.04805", "arxiv:1804.07461", "arxiv:1905.00537", "transformers", "SEAD", "license:apache-2.0" ]
text-classification
false
course5i
null
course5i/SEAD-L-6_H-384_A-12-mnli
17
null
transformers
9,109
--- language: - en license: apache-2.0 tags: - SEAD datasets: - glue - mnli --- ## Paper ## [SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63) Aurthors: *Moyan Mei*, *Rohit Sroch* ## Abstract With the widespread use of pre-trained language models (PLM), there has been increased research on how to make them applicable, especially in limited-resource or low latency high throughput scenarios. One of the dominant approaches is knowledge distillation (KD), where a smaller model is trained by receiving guidance from a large PLM. While there are many successful designs for learning knowledge from teachers, it remains unclear how students can learn better. Inspired by real university teaching processes, in this work we further explore knowledge distillation and propose a very simple yet effective framework, SEAD, to further improve task-specific generalization by utilizing multiple teachers. Our experiments show that SEAD leads to better performance compared to other popular KD methods [[1](https://arxiv.org/abs/1910.01108)] [[2](https://arxiv.org/abs/1909.10351)] [[3](https://arxiv.org/abs/2002.10957)] and achieves comparable or superior performance to its teacher model such as BERT [[4](https://arxiv.org/abs/1810.04805)] on total 13 tasks for the GLUE [[5](https://arxiv.org/abs/1804.07461)] and SuperGLUE [[6](https://arxiv.org/abs/1905.00537)] benchmarks. *Moyan Mei and Rohit Sroch. 2022. [SEAD: Simple ensemble and knowledge distillation framework for natural language understanding](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63). Lattice, THE MACHINE LEARNING JOURNAL by Association of Data Scientists, 3(1).* ## SEAD-L-6_H-384_A-12-mnli This is a student model distilled from [**BERT base**](https://huggingface.co/bert-base-uncased) as teacher by using SEAD framework on **mnli** task. For weights initialization, we used [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) ## All SEAD Checkpoints Other Community Checkpoints: [here](https://huggingface.co/models?search=SEAD) ## Intended uses & limitations More information needed ### Training hyperparameters Please take a look at the `training_args.bin` file ```python $ import torch $ hyperparameters = torch.load(os.path.join('training_args.bin')) ``` ### Evaluation results | eval_m-accuracy | eval_m-runtime | eval_m-samples_per_second | eval_m-steps_per_second | eval_m-loss | eval_m-samples | eval_mm-accuracy | eval_mm-runtime | eval_mm-samples_per_second | eval_mm-steps_per_second | eval_mm-loss | eval_mm-samples | |:---------------:|:--------------:|:-------------------------:|:-----------------------:|:-----------:|:--------------:|:----------------:|:---------------:|:--------------------------:|:------------------------:|:------------:|:---------------:| | 0.8495 | 6.5443 | 1499.776 | 46.911 | 0.4366 | 9815 | 0.8508 | 5.6975 | 1725.678 | 54.059 | 0.4252 | 9832 | ### Framework versions - Transformers >=4.8.0 - Pytorch >=1.6.0 - TensorFlow >=2.5.0 - Flax >=0.3.5 - Datasets >=1.10.2 - Tokenizers >=0.11.6 If you use these models, please cite the following paper: ``` @article{article, author={Mei, Moyan and Sroch, Rohit}, title={SEAD: Simple Ensemble and Knowledge Distillation Framework for Natural Language Understanding}, volume={3}, number={1}, journal={Lattice, The Machine Learning Journal by Association of Data Scientists}, day={26}, year={2022}, month={Feb}, url = {www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63} } ```
EddieChen372/opt-350m-finetuned-jest
3dc4755045e4542f160b047d993af4c98430199b
2022-06-17T15:42:01.000Z
[ "pytorch", "opt", "text-generation", "transformers" ]
text-generation
false
EddieChen372
null
EddieChen372/opt-350m-finetuned-jest
17
null
transformers
9,110
Entry not found
ghadeermobasher/CRAFT-Original-SciBERT-384
b27b834167027381681297aca957e9e02d89c14a
2022-06-13T23:01:55.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
ghadeermobasher
null
ghadeermobasher/CRAFT-Original-SciBERT-384
17
null
transformers
9,111
Entry not found
armandnlp/distilbert-base-uncased-finetuned-emotion
a8ceec6943d0eecbb83dcae6d184416a1d27147d
2022-07-13T15:42:39.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:emotion", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
armandnlp
null
armandnlp/distilbert-base-uncased-finetuned-emotion
17
null
transformers
9,112
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9275 - name: F1 type: f1 value: 0.9273822408882375 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2237 - Accuracy: 0.9275 - F1: 0.9274 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8643 | 1.0 | 250 | 0.3324 | 0.9065 | 0.9025 | | 0.2589 | 2.0 | 500 | 0.2237 | 0.9275 | 0.9274 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
mikegarts/distilgpt2-erichmariaremarque
e7f46cf69554ef4611d8198ba906ca96fe103958
2022-06-23T11:58:00.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-generation
false
mikegarts
null
mikegarts/distilgpt2-erichmariaremarque
17
null
transformers
9,113
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-erichmariaremarque results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-erichmariaremarque This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.10.0 - Datasets 2.3.2 - Tokenizers 0.12.1
nvidia/stt_en_citrinet_1024_gamma_0_25
8248f8cf6658cc888da478950f885617469935cb
2022-06-27T23:10:16.000Z
[ "nemo", "en", "dataset:librispeech_asr", "dataset:fisher_corpus", "dataset:Switchboard-1", "dataset:WSJ-0", "dataset:WSJ-1", "dataset:National Singapore Corpus Part 1", "dataset:National Singapore Corpus Part 6", "arxiv:2104.01721", "automatic-speech-recognition", "speech", "audio", "CTC", "Citrinet", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard", "Riva", "license:cc-by-4.0", "model-index" ]
automatic-speech-recognition
false
nvidia
null
nvidia/stt_en_citrinet_1024_gamma_0_25
17
1
nemo
9,114
--- language: - en library_name: nemo datasets: - librispeech_asr - fisher_corpus - Switchboard-1 - WSJ-0 - WSJ-1 - National Singapore Corpus Part 1 - National Singapore Corpus Part 6 thumbnail: null tags: - automatic-speech-recognition - speech - audio - CTC - Citrinet - Transformer - pytorch - NeMo - hf-asr-leaderboard - Riva license: cc-by-4.0 widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac model-index: - name: stt_en_citrinet_1024_gamma_0_25 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (clean) type: librispeech_asr config: clean split: test args: language: en metrics: - name: Test WER type: wer value: 3.4 - task: type: Automatic Speech Recognition name: automatic-speech-recognition dataset: name: LibriSpeech (other) type: librispeech_asr config: other split: test args: language: en metrics: - name: Test WER type: wer value: 7.6 - task: type: Automatic Speech Recognition name: automatic-speech-recognition dataset: name: Wall Street Journal 92 type: wsj_0 args: language: en metrics: - name: Test WER type: wer value: 2.5 - task: type: Automatic Speech Recognition name: automatic-speech-recognition dataset: name: Wall Street Journal 93 type: wsj_1 args: language: en metrics: - name: Test WER type: wer value: 4.0 - task: type: Automatic Speech Recognition name: automatic-speech-recognition dataset: name: National Singapore Corpus type: nsc_part_1 args: language: en metrics: - name: Test WER type: wer value: 6.2 --- # NVIDIA Streaming Citrinet 1024 (en-US) <style> img { display: inline; } </style> | [![Model architecture](https://img.shields.io/badge/Model_Arch-Citrinet--CTC-lightgrey#model-badge)](#model-architecture) | [![Model size](https://img.shields.io/badge/Params-145M-lightgrey#model-badge)](#model-architecture) | [![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets) | [![Riva Compatible](https://img.shields.io/badge/NVIDIA%20Riva-compatible-brightgreen#model-badge)](#deployment-with-nvidia-riva) | This model transcribes speech in lowercase English alphabet including spaces and apostrophes, and is trained on several thousand hours of English speech data. It is a non-autoregressive "large" variant of Streaming Citrinet, with around 140 million parameters. See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc) for complete architecture details. It is also compatible with NVIDIA Riva for [production-grade server deployments](#deployment-with-nvidia-riva). ## Usage The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed the latest PyTorch version. ``` pip install nemo_toolkit['all'] ``` ### Automatically instantiate the model ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained("nvidia/stt_en_citrinet_1024_gamma_0_25") ``` ### Transcribing using Python First, let's get a sample ``` wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav ``` Then simply do: ``` asr_model.transcribe(['2086-149220-0033.wav']) ``` ### Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/stt_en_citrinet_1024_gamma_0_25" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` ### Input This model accepts 16000 kHz Mono-channel Audio (wav files) as input. ### Output This model provides transcribed speech as a string for a given audio sample. ## Model Architecture Streaming Citrinet-1024 model is a non-autoregressive, streaming variant of Citrinet model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on this model here: [Citrinet Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#citrinet). ## Training The NeMo toolkit [3] was used for training the model for over several hundred epochs. This model was trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/citrinet/citrinet_1024.yaml). The tokenizer for this models was built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). ### Datasets All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several thousand hours of English speech: - Librispeech 960 hours of English speech - Fisher Corpus - Switchboard-1 Dataset - WSJ-0 and WSJ-1 - National Speech Corpus (Part 1, Part 6) Note: older versions of the model may have trained on smaller set of datasets. ## Performance The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. | Version | Tokenizer | Vocabulary Size | LS test-other | LS test-clean | WSJ Eval92 | WSJ Dev93 | NSC Part 1 |Train Dataset | |---------|-----------------------|-----------------|---------------|---------------|------------|-----------|-----|---------| | 1.0.0 | SentencePiece Unigram | 1024 | 7.6 | 3.4 | 2.5 | 4.0 | 6.2 | NeMo ASRSET 1.0 | While deploying with [NVIDIA Riva](https://developer.nvidia.com/riva), you can combine this model with external language models to further improve WER. The WER(%) of the latest model with different language modeling techniques are reported in the following table. ## Limitations Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech that includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. ## Deployment with NVIDIA Riva For the best real-time accuracy, latency, and throughput, deploy the model with [NVIDIA Riva](https://developer.nvidia.com/riva), an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded. Additionally, Riva provides: * World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours * Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization * Streaming speech recognition, Kubernetes compatible scaling, and Enterprise-grade support Check out [Riva live demo](https://developer.nvidia.com/riva#demos). ## References [1] [Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition](https://arxiv.org/abs/2104.01721) [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece) [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
KoichiYasuoka/deberta-base-japanese-wikipedia
e6917abca54a16ae4173b0869137384b298ba633
2022-07-23T14:43:45.000Z
[ "pytorch", "deberta-v2", "fill-mask", "ja", "transformers", "japanese", "masked-lm", "wikipedia", "license:cc-by-sa-4.0", "autotrain_compatible" ]
fill-mask
false
KoichiYasuoka
null
KoichiYasuoka/deberta-base-japanese-wikipedia
17
null
transformers
9,115
--- language: - "ja" tags: - "japanese" - "masked-lm" - "wikipedia" license: "cc-by-sa-4.0" pipeline_tag: "fill-mask" mask_token: "[MASK]" widget: - text: "日本に着いたら[MASK]を訪ねなさい。" --- # deberta-base-japanese-wikipedia ## Model Description This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts. You can fine-tune `deberta-base-japanese-wikipedia` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-wikipedia-luw-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-wikipedia-ud-head), and so on. ## How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia") ``` ## Reference 安岡孝一: [青空文庫DeBERTaモデルによる国語研長単位係り受け解析](http://hdl.handle.net/2433/275409), 東洋学へのコンピュータ利用, 第35回研究セミナー (2022年7月), pp.29-43.
cambridgeltl/mle_wikitext103
2359c00354df30e6290722e8746401bf1b957375
2022-06-26T13:47:20.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
cambridgeltl
null
cambridgeltl/mle_wikitext103
17
null
transformers
9,116
Entry not found
sohomghosh/LIPI_FinSim3_Hypernym
8b77f2907c442cf7f29b374d00c7762ddbbde6d6
2022-06-28T06:54:42.000Z
[ "pytorch", "bert", "feature-extraction", "transformers", "license:mit" ]
feature-extraction
false
sohomghosh
null
sohomghosh/LIPI_FinSim3_Hypernym
17
null
transformers
9,117
--- license: mit --- Note: This model may not perfectly replicate the numbers mentioned in the paper (\cite{chopra-ghosh-2021-term}) as unlike the original one it has been trained with lower batches sizes for fewer epochs. The source code for training will son be made available in https://github.com/sohomghosh/FinSim_Financial_Hypernym_detection ```bibtex @inproceedings{chopra-ghosh-2021-term, title = "Term Expansion and {F}in{BERT} fine-tuning for Hypernym and Synonym Ranking of Financial Terms", author = "Chopra, Ankush and Ghosh, Sohom", booktitle = "Proceedings of the Third Workshop on Financial Technology and Natural Language Processing (FinNLP@IJCAI 2021)", month = "Aug ", year = "2021", address = "Online", publisher = "-", url = "https://aclanthology.org/2021.finnlp-1.8", pages = "46--51", } ``` Use the following code to import this in Transformers: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sohomghosh/LIPI_FinSim3_Hypernym") model = AutoModel.from_pretrained("sohomghosh/LIPI_FinSim3_Hypernym") #Using SentenceTransformers from sentence_transformers import SentenceTransformer model_finlipi = SentenceTransformer('sohomghosh/LIPI_FinSim3_Hypernym') ```
Chemsseddine/bert2gpt2_med_v4
767e8ea8e1d087a3295fd61700bb9843b3c11b4e
2022-06-30T19:49:14.000Z
[ "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
text2text-generation
false
Chemsseddine
null
Chemsseddine/bert2gpt2_med_v4
17
null
transformers
9,118
--- tags: - generated_from_trainer metrics: - rouge model-index: - name: bert2gpt2_med_v4 results: [] --- <img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/> # bert2gpt2_med_v4 This model is a fine-tuned version of [Chemsseddine/bert2gpt2_med_v3](https://huggingface.co/Chemsseddine/bert2gpt2_med_v3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4780 - Rouge1: 36.7502 - Rouge2: 18.5992 - Rougel: 36.2566 - Rougelsum: 36.161 - Gen Len: 22.96 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 169 | 1.4796 | 33.9893 | 16.2462 | 33.5685 | 33.4738 | 22.42 | | No log | 2.0 | 338 | 1.4404 | 34.0811 | 16.219 | 34.0206 | 33.9139 | 22.76 | | 1.0815 | 3.0 | 507 | 1.4078 | 35.2755 | 18.2266 | 34.9186 | 34.9052 | 22.63 | | 1.0815 | 4.0 | 676 | 1.4207 | 34.0146 | 17.4167 | 33.9904 | 33.9735 | 22.92 | | 1.0815 | 5.0 | 845 | 1.4285 | 35.2093 | 17.3269 | 35.1023 | 35.222 | 22.75 | | 0.4699 | 6.0 | 1014 | 1.4607 | 34.5503 | 16.9067 | 34.6404 | 34.5957 | 22.8 | | 0.4699 | 7.0 | 1183 | 1.4469 | 35.0539 | 17.0677 | 34.7607 | 34.8734 | 22.73 | | 0.4699 | 8.0 | 1352 | 1.4632 | 35.2308 | 17.9663 | 35.1657 | 35.1012 | 22.9 | | 0.2522 | 9.0 | 1521 | 1.4734 | 35.5699 | 18.53 | 35.4927 | 35.3747 | 22.84 | | 0.2522 | 10.0 | 1690 | 1.4780 | 36.7502 | 18.5992 | 36.2566 | 36.161 | 22.96 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
dwing/distilbert-base-uncased-finetuned-emotion
d242cc02714f34d36b2e7394bfab24e2cd3c1933
2022-07-01T13:38:31.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:emotion", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
dwing
null
dwing/distilbert-base-uncased-finetuned-emotion
17
null
transformers
9,119
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9335 - name: F1 type: f1 value: 0.9336729469235073 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1616 - Accuracy: 0.9335 - F1: 0.9337 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.1003 | 1.0 | 250 | 0.1854 | 0.931 | 0.9311 | | 0.0891 | 2.0 | 500 | 0.1616 | 0.9335 | 0.9337 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3
nvidia/tts_en_fastpitch
ae570c549ccd53d2f4ac72c472517c242e99e11f
2022-06-29T21:23:52.000Z
[ "nemo", "en", "dataset:ljspeech", "arxiv:2006.06873", "arxiv:2108.10447", "text-to-speech", "speech", "audio", "Transformer", "pytorch", "NeMo", "Riva", "license:cc-by-4.0" ]
text-to-speech
false
nvidia
null
nvidia/tts_en_fastpitch
17
3
nemo
9,120
--- language: - en library_name: nemo datasets: - ljspeech thumbnail: null tags: - text-to-speech - speech - audio - Transformer - pytorch - NeMo - Riva license: cc-by-4.0 --- # NVIDIA FastPitch (en-US) <style> img { display: inline; } </style> | [![Model architecture](https://img.shields.io/badge/Model_Arch-FastPitch--Transformer-lightgrey#model-badge)](#model-architecture) | [![Model size](https://img.shields.io/badge/Params-45M-lightgrey#model-badge)](#model-architecture) | [![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets) | [![Riva Compatible](https://img.shields.io/badge/NVIDIA%20Riva-compatible-brightgreen#model-badge)](#deployment-with-nvidia-riva) | FastPitch [1] is a fully-parallel transformer architecture with prosody control over pitch and individual phoneme duration. Additionally, it uses an unsupervised speech-text aligner [2]. See the [model architecture](#model-architecture) section for complete architecture details. It is also compatible with NVIDIA Riva for [production-grade server deployments](#deployment-with-nvidia-riva). ## Usage The model is available for use in the NeMo toolkit [3] and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed the latest PyTorch version. ``` pip install nemo_toolkit['all'] ``` ### Automatically instantiate the model Note: This model generates only spectrograms and a vocoder is needed to convert the spectrograms to waveforms. In this example HiFiGAN is used. ```python # Load FastPitch from nemo.collections.tts.models import FastPitchModel spec_generator = FastPitchModel.from_pretrained("nvidia/tts_en_fastpitch") # Load vocoder from nemo.collections.tts.models import HifiGanModel model = HifiGanModel.from_pretrained(model_name="nvidia/tts_hifigan") ``` ### Generate audio ```python import soundfile as sf parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.") spectrogram = spec_generator.generate_spectrogram(tokens=parsed) audio = model.convert_spectrogram_to_audio(spec=spectrogram) ``` ### Save the generated audio file ```python # Save the audio to disk in a file called speech.wav sf.write("speech.wav", audio.to('cpu').numpy(), 22050) ``` ### Input This model accepts batches of text. ### Output This model generates mel spectrograms. ## Model Architecture FastPitch is a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours. The model predicts pitch contours during inference. By altering these predictions, the generated speech can be more expressive, better match the semantic of the utterance, and in the end more engaging to the listener. FastPitch is based on a fully-parallel Transformer architecture, with a much higher real-time factor than Tacotron2 for the mel-spectrogram synthesis of a typical utterance. It uses an unsupervised speech-text aligner. ## Training The NeMo toolkit [3] was used for training the models for 1000 epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/tts/fastpitch.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/tts/conf/fastpitch_align_v1.05.yaml). ### Datasets This model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent. ## Performance No performance information is available at this time. ## Limitations This checkpoint only works well with vocoders that were trained on 22050Hz data. Otherwise, the generated audio may be scratchy or choppy-sounding. ## Deployment with NVIDIA Riva For the best real-time accuracy, latency, and throughput, deploy the model with [NVIDIA Riva](https://developer.nvidia.com/riva), an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded. Additionally, Riva provides: * World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours * Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization * Streaming speech recognition, Kubernetes compatible scaling, and Enterprise-grade support Check out [Riva live demo](https://developer.nvidia.com/riva#demos). ## References - [1] [FastPitch: Parallel Text-to-speech with Pitch Prediction](https://arxiv.org/abs/2006.06873) - [2] [One TTS Alignment To Rule Them All](https://arxiv.org/abs/2108.10447) - [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
dminiotas05/distilbert-base-uncased-finetuned-emotion
e3db9113caa03c7b7e8042d19774a16a9ecd903c
2022-06-30T15:49:20.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
dminiotas05
null
dminiotas05/distilbert-base-uncased-finetuned-emotion
17
null
transformers
9,121
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1027 - Accuracy: 0.5447 - F1: 0.4832 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.1848 | 1.0 | 188 | 1.1199 | 0.538 | 0.4607 | | 1.0459 | 2.0 | 376 | 1.1027 | 0.5447 | 0.4832 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
cookpad/mt5-base-indonesia-recipe-query-generation_v4
72515367d6ecb4986bed7d97b693e653058767ef
2022-06-29T21:09:18.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
cookpad
null
cookpad/mt5-base-indonesia-recipe-query-generation_v4
17
null
transformers
9,122
Entry not found
FabianWillner/bert-base-uncased-finetuned-triviaqa
a5c80af433cc43ecad5f203b15d01b34cbaf2bbe
2022-06-30T16:21:05.000Z
[ "pytorch", "tensorboard", "bert", "question-answering", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
FabianWillner
null
FabianWillner/bert-base-uncased-finetuned-triviaqa
17
null
transformers
9,123
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-uncased-finetuned-triviaqa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-triviaqa This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9252 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.9297 | 1.0 | 11195 | 0.9093 | | 0.6872 | 2.0 | 22390 | 0.9252 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
mmdjiji/gpt2-chinese-idioms
7d0f3ae4ee241582a2a762dd69fcd337e0c64f2d
2022-07-01T01:48:28.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "license:gpl-3.0" ]
text-generation
false
mmdjiji
null
mmdjiji/gpt2-chinese-idioms
17
null
transformers
9,124
--- license: gpl-3.0 --- For the detail, see [github:mmdjiji/bert-chinese-idioms](https://github.com/mmdjiji/bert-chinese-idioms).
Abdelmageed95/opt-350m-economy-data
9322da16d4bbf8e256c204e614ae34bc3c1d28bf
2022-07-02T15:12:37.000Z
[ "pytorch", "tensorboard", "opt", "text-generation", "transformers", "generated_from_trainer", "license:other", "model-index" ]
text-generation
false
Abdelmageed95
null
Abdelmageed95/opt-350m-economy-data
17
null
transformers
9,125
--- license: other tags: - generated_from_trainer model-index: - name: opt-350m-economy-data results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opt-350m-economy-data This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.2910 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
srcocotero/RoBERTa-es-qa
5dfb2e5a9f76d30152fcfd8db056ccea05bff8da
2022-07-03T12:27:46.000Z
[ "pytorch", "tensorboard", "bert", "question-answering", "dataset:squad_es", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
srcocotero
null
srcocotero/RoBERTa-es-qa
17
null
transformers
9,126
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_es model-index: - name: RoBERTa-es-qa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # RoBERTa-es-qa This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad_es dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
ajtamayoh/NLP-CIC-WFU_SocialDisNER_fine_tuned_NER_EHR_Spanish_model_Mulitlingual_BERT_v2
ae41afea4dc25c93dde11b602c85edc206045f84
2022-07-04T22:11:42.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
ajtamayoh
null
ajtamayoh/NLP-CIC-WFU_SocialDisNER_fine_tuned_NER_EHR_Spanish_model_Mulitlingual_BERT_v2
17
null
transformers
9,127
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: NLP-CIC-WFU_SocialDisNER_fine_tuned_NER_EHR_Spanish_model_Mulitlingual_BERT_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # NLP-CIC-WFU_SocialDisNER_fine_tuned_NER_EHR_Spanish_model_Mulitlingual_BERT_v2 This model is a fine-tuned version of [ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT](https://huggingface.co/ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1483 - Precision: 0.8699 - Recall: 0.8722 - F1: 0.8711 - Accuracy: 0.9771 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 467 | 0.0851 | 0.8415 | 0.8209 | 0.8310 | 0.9720 | | 0.1011 | 2.0 | 934 | 0.1034 | 0.8681 | 0.8464 | 0.8571 | 0.9744 | | 0.0537 | 3.0 | 1401 | 0.1094 | 0.8527 | 0.8608 | 0.8568 | 0.9753 | | 0.0335 | 4.0 | 1868 | 0.1239 | 0.8617 | 0.8603 | 0.8610 | 0.9751 | | 0.0185 | 5.0 | 2335 | 0.1192 | 0.8689 | 0.8627 | 0.8658 | 0.9756 | | 0.0112 | 6.0 | 2802 | 0.1426 | 0.8672 | 0.8663 | 0.8667 | 0.9765 | | 0.0067 | 7.0 | 3269 | 0.1483 | 0.8699 | 0.8722 | 0.8711 | 0.9771 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
Bismi/t5_squad
32877bb4367a0224e84f1978e12bd87c0ccd6016
2022-07-05T05:11:54.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Bismi
null
Bismi/t5_squad
17
null
transformers
9,128
Entry not found
shubhamitra/distilbert-base-uncased-finetuned-toxic-classification
16d08958bda0f80f0111461fe8b47c778369ac92
2022-07-05T05:57:03.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
shubhamitra
null
shubhamitra/distilbert-base-uncased-finetuned-toxic-classification
17
null
transformers
9,129
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-toxic-classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-toxic-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | No log | 1.0 | 498 | 0.0419 | 0.7754 | 0.8736 | 0.9235 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Tokenizers 0.12.1
leminhds/distilbert-base-uncased-finetuned-emotion
c74b91253e5acdf472e4bf39f1f95720e696c8d9
2022-07-26T20:50:41.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:emotion", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
leminhds
null
leminhds/distilbert-base-uncased-finetuned-emotion
17
null
transformers
9,130
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1677 - eval_accuracy: 0.924 - eval_f1: 0.9238 - eval_runtime: 2.5188 - eval_samples_per_second: 794.026 - eval_steps_per_second: 12.704 - epoch: 1.0 - step: 250 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0 - Datasets 2.3.2 - Tokenizers 0.12.1
upsalite/xlm-roberta-base-finetuned-emotion-2-labels
9b0e1989f0cff43dd8628fc538577fd7d217f143
2022-07-13T09:56:05.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "text-classification", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
upsalite
null
upsalite/xlm-roberta-base-finetuned-emotion-2-labels
17
null
transformers
9,131
--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: xlm-roberta-base-finetuned-emotion-2-labels results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-emotion-2-labels This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1200 - Accuracy: 0.835 - F1: 0.8335 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.6973 | 1.0 | 25 | 0.6917 | 0.5 | 0.3333 | | 0.6626 | 2.0 | 50 | 0.5690 | 0.745 | 0.7431 | | 0.5392 | 3.0 | 75 | 0.4598 | 0.76 | 0.7591 | | 0.4253 | 4.0 | 100 | 0.4313 | 0.8 | 0.7993 | | 0.2973 | 5.0 | 125 | 0.5872 | 0.795 | 0.7906 | | 0.2327 | 6.0 | 150 | 0.4951 | 0.805 | 0.8049 | | 0.173 | 7.0 | 175 | 0.6095 | 0.815 | 0.8142 | | 0.1159 | 8.0 | 200 | 0.6523 | 0.825 | 0.8246 | | 0.0791 | 9.0 | 225 | 0.6651 | 0.825 | 0.8243 | | 0.0557 | 10.0 | 250 | 0.8242 | 0.83 | 0.8286 | | 0.0643 | 11.0 | 275 | 0.6710 | 0.825 | 0.8243 | | 0.0507 | 12.0 | 300 | 0.7729 | 0.83 | 0.8294 | | 0.0239 | 13.0 | 325 | 0.8618 | 0.83 | 0.8283 | | 0.0107 | 14.0 | 350 | 0.9683 | 0.835 | 0.8335 | | 0.0233 | 15.0 | 375 | 1.0850 | 0.825 | 0.8227 | | 0.0134 | 16.0 | 400 | 0.9801 | 0.835 | 0.8343 | | 0.0122 | 17.0 | 425 | 1.0427 | 0.845 | 0.8439 | | 0.0046 | 18.0 | 450 | 1.0867 | 0.84 | 0.8387 | | 0.0038 | 19.0 | 475 | 1.0950 | 0.83 | 0.8289 | | 0.002 | 20.0 | 500 | 1.1200 | 0.835 | 0.8335 | ### Framework versions - Transformers 4.19.0 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.12.1
michauhl/distilbert-base-uncased-finetuned-emotion
060607007290789050cb1047e6d0b707de1e28ad
2022-07-13T12:57:33.000Z
[ "pytorch", "distilbert", "text-classification", "dataset:emotion", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
michauhl
null
michauhl/distilbert-base-uncased-finetuned-emotion
17
null
transformers
9,132
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9405 - name: F1 type: f1 value: 0.9404976918144629 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1891 - Accuracy: 0.9405 - F1: 0.9405 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.1344 | 1.0 | 1000 | 0.1760 | 0.933 | 0.9331 | | 0.0823 | 2.0 | 2000 | 0.1891 | 0.9405 | 0.9405 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0.post202 - Datasets 2.3.2 - Tokenizers 0.11.0
SkolkovoInstitute/GenChal_2022_nigula
abb178538d7faf355bedb1fc69bddabb1ce5bbdb
2022-07-11T11:22:22.000Z
[ "pytorch", "t5", "text2text-generation", "en", "transformers", "feedback comment generation for writing learning", "autotrain_compatible" ]
text2text-generation
false
SkolkovoInstitute
null
SkolkovoInstitute/GenChal_2022_nigula
17
null
transformers
9,133
--- language: - en tags: - feedback comment generation for writing learning licenses: - cc-by-nc-sa --- ## Model overview This model was trained in terms of [GenChal 2022: Feedback Comment Generation for Writing Learning](https://fcg.sharedtask.org/) shared task In this task, the model gets the string with text with the error and the exact span of the error and should return the comment in natural language, which explains the nature of the error. ## Model training details #### Data The data was provided in the following way ``` input sentence [\t] offset range [\t] feedback comment ``` Here are some examples ``` The smoke flow my face . 10:17 When the <verb> <<flow>> is used as an <intransitive verb> to express ''to move in a stream'', a <preposition> needs to be placed to indicate the direction. 'To' and 'towards' are <prepositions> that indicate direction. I want to stop smoking during driving bicycle . 23:29 A <gerund> does not normally follow the <preposition> <<during>>. Think of an expression using the <conjunction> 'while' instead of a <preposition>. ``` Grammar termins are highlighted with '< ... >' marks and word examples - with '<< ... >>' #### Data preprocessing We lowercased the text, split it from any punctuation, including task specific marks (<< >>) and explicitly pointed out the error in the original text using << >>. ``` the smoke < < flow > > < < my > > face . 10:17 When the < verb > < < flow > > is used as an < intransitive verb > to express '' to move in a stream '', a < preposition > needs to be placed to indicate the direction. ' to ' and ' towards ' are < prepositions > that indicate direction . i want to stop smoking < < during > > driving bicycle . 23:29 a < gerund > does not normally follow the < preposition > < < during > > . think of an expression using the < conjunction > ' while ' instead of a < preposition > . ``` #### Data augmentation The main feature of our training pipeline was data augmentation. The idea of the augmentation is as follows: we cut the existing text with error after the last word which was syntactically connected to the words inside the error span (syntactic dependencies were automatically parsed with spacy) and this cut version of the text with error was used as a prompt for language model (we used [GPT-Neo 1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B)). Using both initial and augmented data we fine-tuned [t5-large](https://huggingface.co/t5-large). ## How to use ```python from transformers import T5ForConditionalGeneration, AutoTokenizer text_with_error = 'I want to stop smoking during driving bicycle .' error_span = '23:29' off1, off2 = list(map(int,error_span.split(":"))) text_with_error_pointed = text_with_error [:off1] + "< < " + re.sub("\s+", " > > < < ", text_with_error [off1:off2].strip()) + " > > " + text_with_error[off2:] text_with_error_pointed = re.sub("\s+", " ", text_with_error_pointed .strip()).lower() tokenizer = AutoTokenizer.from_pretrained("SkolkovoInstitute/GenChal_2022_nigula") model = T5ForConditionalGeneration.from_pretrained("SkolkovoInstitute/GenChal_2022_nigula").cuda(); model.eval(); def paraphrase(text, model, temperature=1.0, beams=3): texts = [text] if isinstance(text, str) else text inputs = tokenizer(texts, return_tensors='pt', padding=True)['input_ids'].to(model.device) result = model.generate( inputs, # num_return_sequences=n or 1, do_sample=False, temperature=temperature, repetition_penalty=1.1, max_length=int(inputs.shape[1] * 3) , # bad_words_ids=[[2]], # unk num_beams=beams, ) texts = [tokenizer.decode(r, skip_special_tokens=True) for r in result] if isinstance(text, str): return texts[0] return texts paraphrase([pointed_example], model) # expected output: ["a gerund > does not normally follow the preposition > during > >. think of an expression using the conjunction >'while'instead of a preposition >."] ``` ## Licensing Information [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png
kinanmartin/xlm-roberta-large-ner-hrl-finetuned-ner
13e9c2a21b0d892309d5187cfc4622c391a7795b
2022-07-11T17:29:06.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "token-classification", "dataset:toydata", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
token-classification
false
kinanmartin
null
kinanmartin/xlm-roberta-large-ner-hrl-finetuned-ner
17
null
transformers
9,134
--- tags: - generated_from_trainer datasets: - toydata metrics: - precision - recall - f1 - accuracy model-index: - name: xlm-roberta-large-ner-hrl-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: toydata type: toydata args: SDN metrics: - name: Precision type: precision value: 0.9132452695465905 - name: Recall type: recall value: 0.9205854126679462 - name: F1 type: f1 value: 0.9169006511739053 - name: Accuracy type: accuracy value: 0.9784804945824268 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-large-ner-hrl-finetuned-ner This model is a fine-tuned version of [Davlan/xlm-roberta-large-ner-hrl](https://huggingface.co/Davlan/xlm-roberta-large-ner-hrl) on the toydata dataset. It achieves the following results on the evaluation set: - Loss: 0.0944 - Precision: 0.9132 - Recall: 0.9206 - F1: 0.9169 - Accuracy: 0.9785 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 408 | 0.0900 | 0.8508 | 0.9303 | 0.8888 | 0.9719 | | 0.1087 | 2.0 | 816 | 0.0827 | 0.9043 | 0.9230 | 0.9136 | 0.9783 | | 0.0503 | 3.0 | 1224 | 0.0944 | 0.9132 | 0.9206 | 0.9169 | 0.9785 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
nloc2578/new_ans1
72067b19f52d42ca8a919243d1c39a4158d4fb33
2022-07-11T21:36:56.000Z
[ "pytorch", "tensorboard", "pegasus", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
text2text-generation
false
nloc2578
null
nloc2578/new_ans1
17
null
transformers
9,135
--- tags: - generated_from_trainer model-index: - name: new_ans1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # new_ans1 This model is a fine-tuned version of [nloc2578/new1](https://huggingface.co/nloc2578/new1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Tokenizers 0.12.1
murtaza-jafri/DialoGPT-medium-Joshua
36d8ecd55e2a9bd376db423e6f2298da34b644a6
2022-07-12T10:52:55.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
murtaza-jafri
null
murtaza-jafri/DialoGPT-medium-Joshua
17
null
transformers
9,136
Entry not found
Evelyn18/distilbert-base-uncased-becasv3-1
745312889cac361ddd706beeeb93d3c01a6986fa
2022-07-13T18:13:27.000Z
[ "pytorch", "tensorboard", "distilbert", "question-answering", "dataset:becasv3", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
Evelyn18
null
Evelyn18/distilbert-base-uncased-becasv3-1
17
null
transformers
9,137
--- license: apache-2.0 tags: - generated_from_trainer datasets: - becasv3 model-index: - name: distilbert-base-uncased-becasv3-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-becasv3-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv3 dataset. It achieves the following results on the evaluation set: - Loss: 3.1086 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 8 | 5.1063 | | No log | 2.0 | 16 | 4.4615 | | No log | 3.0 | 24 | 3.9351 | | No log | 4.0 | 32 | 3.5490 | | No log | 5.0 | 40 | 3.3299 | | No log | 6.0 | 48 | 3.2148 | | No log | 7.0 | 56 | 3.1292 | | No log | 8.0 | 64 | 3.1086 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
abdulmatinomotoso/headline_generator
f1befd008c4dd15630d779fab7bcee04c6350c9d
2022-07-13T10:57:59.000Z
[ "pytorch", "tensorboard", "pegasus", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
text2text-generation
false
abdulmatinomotoso
null
abdulmatinomotoso/headline_generator
17
null
transformers
9,138
--- tags: - generated_from_trainer model-index: - name: headline_generator results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # headline_generator This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3298 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5368 | 0.57 | 500 | 0.3298 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
jhonparra18/bert-base-cased-fine-tuning-cvs-hf-studio-name
a9c8183b916c7d9fe6b37eb42365705d6bd2f8ab
2022-07-16T02:44:09.000Z
[ "pytorch", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
jhonparra18
null
jhonparra18/bert-base-cased-fine-tuning-cvs-hf-studio-name
17
null
transformers
9,139
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: bert-base-cased-fine-tuning-cvs-hf-studio-name results: [] widget: - text: "Egresado de la carrera Ingeniería en Computación Conocimientos de lenguajes HTML, CSS, Javascript y MySQL. Experiencia trabajando en ámbitos de redes de pequeña y mediana escala. Inglés Hablado nivel básico, escrito nivel intermedio.HTML, CSS y JavaScript. Realidad aumentada. Lenguaje R. HTML5, JavaScript y Nodejs" --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-fine-tuning-cvs-hf-studio-name This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2601 - Accuracy: 0.6500 - F1: 0.6500 - Precision: 0.6500 - Recall: 0.6500 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 1.4407 | 0.24 | 500 | 1.5664 | 0.5528 | 0.5528 | 0.5528 | 0.5528 | | 1.3055 | 0.49 | 1000 | 1.4891 | 0.5745 | 0.5745 | 0.5745 | 0.5745 | | 1.373 | 0.73 | 1500 | 1.3634 | 0.6180 | 0.6180 | 0.6180 | 0.6180 | | 1.3621 | 0.98 | 2000 | 1.3768 | 0.6139 | 0.6139 | 0.6139 | 0.6139 | | 1.1677 | 1.22 | 2500 | 1.3330 | 0.6395 | 0.6395 | 0.6395 | 0.6395 | | 1.0826 | 1.47 | 3000 | 1.4003 | 0.6146 | 0.6146 | 0.6146 | 0.6146 | | 1.0968 | 1.71 | 3500 | 1.2601 | 0.6500 | 0.6500 | 0.6500 | 0.6500 | | 1.0896 | 1.96 | 4000 | 1.2826 | 0.6564 | 0.6564 | 0.6564 | 0.6564 | | 0.8572 | 2.2 | 4500 | 1.3254 | 0.6569 | 0.6569 | 0.6569 | 0.6569 | | 0.822 | 2.44 | 5000 | 1.3024 | 0.6571 | 0.6571 | 0.6571 | 0.6571 | | 0.8022 | 2.69 | 5500 | 1.2971 | 0.6608 | 0.6608 | 0.6608 | 0.6608 | | 0.834 | 2.93 | 6000 | 1.2900 | 0.6630 | 0.6630 | 0.6630 | 0.6630 | ### Framework versions - Transformers 4.19.0 - Pytorch 1.8.2+cu111 - Datasets 1.6.2 - Tokenizers 0.12.1
juliensimon/distilbert-imdb-mlflow
e8b279db024af702e76721345effa060378ba71d
2022-07-15T13:04:46.000Z
[ "pytorch", "distilbert", "text-classification", "dataset:imdb", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
juliensimon
null
juliensimon/distilbert-imdb-mlflow
17
null
transformers
9,140
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-imdb-mlflow results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-imdb-mlflow This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the imdb dataset. MLflow logs are included. To visualize them, just clone the repo and run : ``` mlflow ui ``` ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.10.0 - Datasets 2.3.2 - Tokenizers 0.12.1
jhonparra18/xlm-roberta-base-cv-studio_name-pooler
484d34a6c7ebde2251c2406030f0345562244744
2022-07-26T19:15:36.000Z
[ "pytorch", "xlm-roberta", "text-classification", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
jhonparra18
null
jhonparra18/xlm-roberta-base-cv-studio_name-pooler
17
null
transformers
9,141
--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlm-roberta-base-cv-studio_name-pooler results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-cv-studio_name-pooler This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1268 - Accuracy: 0.6758 - F1 Micro: 0.6758 - F1 Macro: 0.3836 - Precision Micro: 0.6758 - Recall Micro: 0.6758 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | Precision Micro | Recall Micro | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|:------------:| | 2.0159 | 1.59 | 1000 | 1.6867 | 0.4801 | 0.4801 | 0.1389 | 0.4801 | 0.4801 | | 1.5921 | 3.18 | 2000 | 1.4606 | 0.5342 | 0.5342 | 0.2238 | 0.5342 | 0.5342 | | 1.447 | 4.77 | 3000 | 1.3170 | 0.6114 | 0.6114 | 0.3021 | 0.6114 | 0.6114 | | 1.3441 | 6.36 | 4000 | 1.2777 | 0.6181 | 0.6181 | 0.3264 | 0.6181 | 0.6181 | | 1.2847 | 7.95 | 5000 | 1.1902 | 0.6555 | 0.6555 | 0.3495 | 0.6555 | 0.6555 | | 1.2296 | 9.54 | 6000 | 1.1867 | 0.6635 | 0.6635 | 0.3608 | 0.6635 | 0.6635 | | 1.1965 | 11.13 | 7000 | 1.1426 | 0.6683 | 0.6683 | 0.3728 | 0.6683 | 0.6683 | | 1.1547 | 12.72 | 8000 | 1.1419 | 0.6687 | 0.6687 | 0.3743 | 0.6687 | 0.6687 | | 1.1677 | 14.31 | 9000 | 1.1268 | 0.6758 | 0.6758 | 0.3836 | 0.6758 | 0.6758 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.8.1+cu111 - Datasets 1.6.2 - Tokenizers 0.12.1
annahaz/distilbert-base-multilingual-cased-finetuned-misogyny-sexism-out-of-sample-test-opt-EN
23e08ae47a484b4c6a64df913e9eedf62f5a4952
2022-07-26T14:17:40.000Z
[ "pytorch", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
annahaz
null
annahaz/distilbert-base-multilingual-cased-finetuned-misogyny-sexism-out-of-sample-test-opt-EN
17
null
transformers
9,142
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: distilbert-base-multilingual-cased-finetuned-misogyny-sexism-out-of-sample-test-opt-EN results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-multilingual-cased-finetuned-misogyny-sexism-out-of-sample-test-opt-EN This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0225 - Accuracy: 0.8312 - F1: 0.3169 - Precision: 0.2393 - Recall: 0.4689 - Mae: 0.1688 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| | 0.3654 | 1.0 | 2395 | 0.3117 | 0.8590 | 0.3599 | 0.2898 | 0.4747 | 0.1410 | | 0.3289 | 2.0 | 4790 | 0.4348 | 0.7582 | 0.3375 | 0.2188 | 0.7374 | 0.2418 | | 0.2615 | 3.0 | 7185 | 0.3927 | 0.8425 | 0.3448 | 0.2642 | 0.4961 | 0.1575 | | 0.1982 | 4.0 | 9580 | 0.8717 | 0.7641 | 0.3170 | 0.2091 | 0.6556 | 0.2359 | | 0.176 | 5.0 | 11975 | 0.5349 | 0.8573 | 0.3328 | 0.2731 | 0.4261 | 0.1427 | | 0.1422 | 6.0 | 14370 | 0.8742 | 0.8130 | 0.3062 | 0.2218 | 0.4942 | 0.1870 | | 0.1161 | 7.0 | 16765 | 0.7036 | 0.8700 | 0.2870 | 0.2648 | 0.3132 | 0.1300 | | 0.1218 | 8.0 | 19160 | 0.7793 | 0.8512 | 0.3113 | 0.2537 | 0.4027 | 0.1488 | | 0.0991 | 9.0 | 21555 | 0.8698 | 0.8518 | 0.3153 | 0.2567 | 0.4086 | 0.1482 | | 0.092 | 10.0 | 23950 | 1.0225 | 0.8312 | 0.3169 | 0.2393 | 0.4689 | 0.1688 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
YumeAyasaki/DialoGPT-small-rubybot
8dce32ec7182c95cc7b2470d4ba9f5aebe330f2e
2022-07-16T05:47:17.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
YumeAyasaki
null
YumeAyasaki/DialoGPT-small-rubybot
17
null
transformers
9,143
--- tags: - conversational --- # Nothing nothing...
fadhilarkn/distilbert-base-uncased-finetuned-ner
61fd0cac02cd581d88982d76eb7572a11bc40f84
2022-07-17T09:45:18.000Z
[ "pytorch", "tensorboard", "distilbert", "token-classification", "dataset:conll2003", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
fadhilarkn
null
fadhilarkn/distilbert-base-uncased-finetuned-ner
17
null
transformers
9,144
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9276948590381426 - name: Recall type: recall value: 0.9386956035350711 - name: F1 type: f1 value: 0.9331628113879005 - name: Accuracy type: accuracy value: 0.9842883695807584 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0574 - Precision: 0.9277 - Recall: 0.9387 - F1: 0.9332 - Accuracy: 0.9843 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2384 | 1.0 | 878 | 0.0701 | 0.9130 | 0.9220 | 0.9175 | 0.9803 | | 0.0494 | 2.0 | 1756 | 0.0593 | 0.9222 | 0.9314 | 0.9268 | 0.9829 | | 0.0301 | 3.0 | 2634 | 0.0574 | 0.9277 | 0.9387 | 0.9332 | 0.9843 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
khosseini/bert_1875_1890
0b0af9f9ac16470ccbcb030a3e25ffdce54d63e2
2022-07-18T09:37:54.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
khosseini
null
khosseini/bert_1875_1890
17
null
transformers
9,145
# Neural Language Models for Nineteenth-Century English: bert_1875_1890 ## Introduction BERT model trained on a large historical dataset of books in English, published between 1875-1890 and comprised of ~1.3 billion tokens. - Data paper: http://doi.org/10.5334/johd.48 - Github repository: https://github.com/Living-with-machines/histLM ## License The models are released under open license CC BY 4.0, available at https://creativecommons.org/licenses/by/4.0/legalcode. ## Funding Statement This work was supported by Living with Machines (AHRC grant AH/S01179X/1) and The Alan Turing Institute (EPSRC grant EP/N510129/1). ## Dataset creators Kasra Hosseini, Kaspar Beelen and Mariona Coll Ardanuy (The Alan Turing Institute) preprocessed the text, created a database, trained and fine-tuned language models as described in the accompanying paper. Giovanni Colavizza (University of Amsterdam), David Beavan (The Alan Turing Institute) and James Hetherington (University College London) helped with planning, accessing the datasets and designing the experiments.
khosseini/bert_1890_1900
e077ccac28833a7356a55c468fee93d2f3f454dd
2022-07-18T09:41:11.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
khosseini
null
khosseini/bert_1890_1900
17
0
transformers
9,146
# Neural Language Models for Nineteenth-Century English: bert_1890_1900 ## Introduction BERT model trained on a large historical dataset of books in English, published between 1890-1900 and comprised of ~1.1 billion tokens. - Data paper: http://doi.org/10.5334/johd.48 - Github repository: https://github.com/Living-with-machines/histLM ## License The models are released under open license CC BY 4.0, available at https://creativecommons.org/licenses/by/4.0/legalcode. ## Funding Statement This work was supported by Living with Machines (AHRC grant AH/S01179X/1) and The Alan Turing Institute (EPSRC grant EP/N510129/1). ## Dataset creators Kasra Hosseini, Kaspar Beelen and Mariona Coll Ardanuy (The Alan Turing Institute) preprocessed the text, created a database, trained and fine-tuned language models as described in the accompanying paper. Giovanni Colavizza (University of Amsterdam), David Beavan (The Alan Turing Institute) and James Hetherington (University College London) helped with planning, accessing the datasets and designing the experiments.
Evelyn18/roberta-base-spanish-squades-modelo-robertav0
270cfad02779c27eb491232757663e7e2e6bbab2
2022-07-18T16:01:20.000Z
[ "pytorch", "tensorboard", "roberta", "question-answering", "dataset:becasv2", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
Evelyn18
null
Evelyn18/roberta-base-spanish-squades-modelo-robertav0
17
null
transformers
9,147
--- tags: - generated_from_trainer datasets: - becasv2 model-index: - name: roberta-base-spanish-squades-modelo-robertav0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-spanish-squades-modelo-robertav0 This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://huggingface.co/IIC/roberta-base-spanish-squades) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 2.7628 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 11 - eval_batch_size: 11 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 6 | 2.1175 | | No log | 2.0 | 12 | 1.7427 | | No log | 3.0 | 18 | 2.0810 | | No log | 4.0 | 24 | 2.3820 | | No log | 5.0 | 30 | 2.5007 | | No log | 6.0 | 36 | 2.6782 | | No log | 7.0 | 42 | 2.7578 | | No log | 8.0 | 48 | 2.7703 | | No log | 9.0 | 54 | 2.7654 | | No log | 10.0 | 60 | 2.7628 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
jordyvl/bert-base-portuguese-cased_harem-selective-lowC-CRF-first-ner
6f93ea9c57bc55dc32cca2f40e6a5405b098a4af
2022-07-19T15:32:43.000Z
[ "pytorch", "tensorboard", "bert", "dataset:harem", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
null
false
jordyvl
null
jordyvl/bert-base-portuguese-cased_harem-selective-lowC-CRF-first-ner
17
null
transformers
9,148
--- license: mit tags: - generated_from_trainer datasets: - harem metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-portuguese-cased_harem-selective-lowC-CRF-first-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-portuguese-cased_harem-selective-lowC-CRF-first-ner This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the harem dataset. It achieves the following results on the evaluation set: - Loss: 0.0687 - Precision: 0.8030 - Recall: 0.8933 - F1: 0.8457 - Accuracy: 0.9748 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0646 | 1.0 | 2517 | 0.0924 | 0.7822 | 0.8876 | 0.8316 | 0.9670 | | 0.0263 | 2.0 | 5034 | 0.0644 | 0.7598 | 0.8708 | 0.8115 | 0.9685 | | 0.0234 | 3.0 | 7551 | 0.0687 | 0.8030 | 0.8933 | 0.8457 | 0.9748 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1
kabelomalapane/En-Nso_update2
59c5b5e6361b1ff87b0f99e2d5e8e6aeeb55cd0f
2022-07-20T18:37:27.000Z
[ "pytorch", "tensorboard", "marian", "text2text-generation", "transformers", "translation", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
translation
false
kabelomalapane
null
kabelomalapane/En-Nso_update2
17
null
transformers
9,149
--- license: apache-2.0 tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: En-Nso_update2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # En-Nso_update2 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-nso](https://huggingface.co/Helsinki-NLP/opus-mt-en-nso) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4199 - Bleu: 24.4776 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 3.6661 | 1.0 | 865 | 3.0081 | 17.6871 | | 2.7495 | 2.0 | 1730 | 2.7725 | 20.1475 | | 2.4533 | 3.0 | 2595 | 2.6433 | 22.5433 | | 2.3203 | 4.0 | 3460 | 2.5625 | 22.9963 | | 2.1356 | 5.0 | 4325 | 2.5190 | 23.5696 | | 2.0258 | 6.0 | 5190 | 2.4881 | 23.8367 | | 1.9481 | 7.0 | 6055 | 2.4641 | 24.0611 | | 1.8769 | 8.0 | 6920 | 2.4526 | 24.3214 | | 1.8211 | 9.0 | 7785 | 2.4392 | 24.5300 | | 1.7689 | 10.0 | 8650 | 2.4307 | 24.4627 | | 1.7314 | 11.0 | 9515 | 2.4254 | 24.4936 | | 1.7 | 12.0 | 10380 | 2.4243 | 24.4673 | | 1.6695 | 13.0 | 11245 | 2.4202 | 24.5613 | | 1.6562 | 14.0 | 12110 | 2.4200 | 24.4886 | | 1.6446 | 15.0 | 12975 | 2.4199 | 24.4711 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
acho0057/sentiment_analysis_custom
f981b2566fd6b44d66be482d48a4ba8c7f2b4c93
2022-07-22T08:51:18.000Z
[ "pytorch", "tf", "roberta", "text-classification", "transformers" ]
text-classification
false
acho0057
null
acho0057/sentiment_analysis_custom
17
null
transformers
9,150
huggingtweets/luciengreaves-pontifex
7d3ac0d21352d63d2ec67a6f3200997833633792
2022-07-23T00:00:09.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/luciengreaves-pontifex
17
1
transformers
9,151
--- language: en thumbnail: http://www.huggingtweets.com/luciengreaves-pontifex/1658534403996/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/666311094256971779/rhb7qkCD_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/507818066814590976/KNG-IkT9_400x400.jpeg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Lucien Greaves & Pope Francis</div> <div style="text-align: center; font-size: 14px;">@luciengreaves-pontifex</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Lucien Greaves & Pope Francis. | Data | Lucien Greaves | Pope Francis | | --- | --- | --- | | Tweets downloaded | 3197 | 3250 | | Retweets | 536 | 0 | | Short tweets | 379 | 103 | | Tweets kept | 2282 | 3147 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/q0nkdf60/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @luciengreaves-pontifex's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2y98dgmx) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2y98dgmx/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/luciengreaves-pontifex') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Intel/bert-base-uncased-squad-int8-static
de75d12ae3543c0564565b38ec9f4163b13b7417
2022-07-25T02:42:58.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "int8", "Intel® Neural Compressor", "PostTrainingStatic", "license:apache-2.0", "autotrain_compatible" ]
question-answering
false
Intel
null
Intel/bert-base-uncased-squad-int8-static
17
null
transformers
9,152
--- license: apache-2.0 tags: - int8 - Intel® Neural Compressor - PostTrainingStatic datasets: - squad metrics: - f1 --- # INT8 BERT base uncased finetuned on Squad ### Post-training static quantization This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [jimypbr/bert-base-uncased-squad](https://huggingface.co/jimypbr/bert-base-uncased-squad). The calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch size 8, so the real sampling size is 304. The linear modules **bert.encoder.layer.2.intermediate.dense**, **bert.encoder.layer.4.intermediate.dense**, **bert.encoder.layer.9.output.dense**, **bert.encoder.layer.10.output.dense** fall back to fp32 to meet the 1% relative accuracy loss. ### Test result | |INT8|FP32| |---|:---:|:---:| | **Accuracy (eval-f1)** |87.3006|88.1030| | **Model size (MB)** |139|436| ### Load with Intel® Neural Compressor: ```python from neural_compressor.utils.load_huggingface import OptimizedModel int8_model = OptimizedModel.from_pretrained( 'Intel/bert-base-uncased-squad-int8-static', ) ```
doya/klue-sentiment-nsmc
e78d67b0dd3b4260a74806e934cc0adde05ef3ef
2022-07-25T07:30:58.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
doya
null
doya/klue-sentiment-nsmc
17
null
transformers
9,153
Entry not found
BramVanroy/robbert-v2-dutch-base-hebban-reviews
f28d32f1c459cb7879df74ef865ce98970384a24
2022-07-29T09:39:53.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "nl", "dataset:BramVanroy/hebban-reviews", "transformers", "sentiment-analysis", "dutch", "text", "license:mit", "model-index" ]
text-classification
false
BramVanroy
null
BramVanroy/robbert-v2-dutch-base-hebban-reviews
17
null
transformers
9,154
--- datasets: - BramVanroy/hebban-reviews language: - nl license: mit metrics: - accuracy - f1 - precision - qwk - recall model-index: - name: robbert-v2-dutch-base-hebban-reviews results: - dataset: config: filtered_sentiment name: BramVanroy/hebban-reviews - filtered_sentiment - 2.0.0 revision: 2.0.0 split: test type: BramVanroy/hebban-reviews metrics: - name: Test accuracy type: accuracy value: 0.8070512820512821 - name: Test f1 type: f1 value: 0.8144966061997005 - name: Test precision type: precision value: 0.8275999429062602 - name: Test qwk type: qwk value: 0.7336245557372719 - name: Test recall type: recall value: 0.8070512820512821 task: name: sentiment analysis type: text-classification tags: - sentiment-analysis - dutch - text widget: - text: Wauw, wat een leuk boek! Ik heb me er er goed mee vermaakt. - text: Nee, deze vond ik niet goed. De auteur doet zijn best om je als lezer mee te trekken in het verhaal maar mij overtuigt het alleszins niet. - text: Ik vind het niet slecht maar de schrijfstijl trekt me ook niet echt aan. Het wordt een beetje saai vanaf het vijfde hoofdstuk --- # robbert-v2-dutch-base-hebban-reviews # Dataset - dataset_name: BramVanroy/hebban-reviews - dataset_config: filtered_sentiment - dataset_revision: 2.0.0 - labelcolumn: review_sentiment - textcolumn: review_text_without_quotes # Training - optim: adamw_hf - learning_rate: 5e-05 - per_device_train_batch_size: 64 - per_device_eval_batch_size: 64 - gradient_accumulation_steps: 1 - max_steps: 5001 - save_steps: 500 - metric_for_best_model: qwk # Best checkedpoint based on validation - best_metric: 0.7412639349881154 - best_model_checkpoint: trained/hebban-reviews/robbert-v2-dutch-base/checkpoint-3500 # Test results of best checkpoint - accuracy: 0.8070512820512821 - f1: 0.8144966061997005 - precision: 0.8275999429062602 - qwk: 0.7336245557372719 - recall: 0.8070512820512821 ## Confusion matric ![cfm](fig/test_confusion_matrix.png) ## Normalized confusion matrix ![norm cfm](fig/test_confusion_matrix_norm.png) # Environment - cuda_capabilities: 8.0; 8.0 - cuda_device_count: 2 - cuda_devices: NVIDIA A100-SXM4-80GB; NVIDIA A100-SXM4-80GB - finetuner_commit: 66294c815326c93682003119534cb72009f558c2 - platform: Linux-4.18.0-305.49.1.el8_4.x86_64-x86_64-with-glibc2.28 - python_version: 3.9.5 - toch_version: 1.10.0 - transformers_version: 4.21.0
huggingtweets/acrasials_art
f1d7e189e7b4631ce3788585a1666a45f46edd8b
2022-07-26T14:30:32.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/acrasials_art
17
null
transformers
9,155
--- language: en thumbnail: http://www.huggingtweets.com/acrasials_art/1658845828038/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1459339266060918789/mjxa2TwP_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Acrasial! 🫡</div> <div style="text-align: center; font-size: 14px;">@acrasials_art</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Acrasial! 🫡. | Data | Acrasial! 🫡 | | --- | --- | | Tweets downloaded | 3235 | | Retweets | 1321 | | Short tweets | 492 | | Tweets kept | 1422 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3imbmus0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @acrasials_art's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/asit6thi) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/asit6thi/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/acrasials_art') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
AbidHasan95/movieHunt4-ner
65203f8a3c7701f8ca1dac9a9bfcad9cadec36e6
2022-07-29T09:53:57.000Z
[ "pytorch", "tensorboard", "distilbert", "token-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
AbidHasan95
null
AbidHasan95/movieHunt4-ner
17
null
transformers
9,156
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: movieHunt4-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # movieHunt4-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0005 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 48 | 0.0284 | 0.9959 | 0.9959 | 0.9959 | 0.9974 | | No log | 2.0 | 96 | 0.0060 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 3.0 | 144 | 0.0034 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 4.0 | 192 | 0.0025 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 5.0 | 240 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 6.0 | 288 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 7.0 | 336 | 0.0014 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 8.0 | 384 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 9.0 | 432 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 10.0 | 480 | 0.0010 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0168 | 11.0 | 528 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0168 | 12.0 | 576 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0168 | 13.0 | 624 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0168 | 14.0 | 672 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0168 | 15.0 | 720 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0168 | 16.0 | 768 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0168 | 17.0 | 816 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0168 | 18.0 | 864 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0168 | 19.0 | 912 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0168 | 20.0 | 960 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0014 | 21.0 | 1008 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0014 | 22.0 | 1056 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0014 | 23.0 | 1104 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0014 | 24.0 | 1152 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0014 | 25.0 | 1200 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0014 | 26.0 | 1248 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0014 | 27.0 | 1296 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0014 | 28.0 | 1344 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0014 | 29.0 | 1392 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0014 | 30.0 | 1440 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
BigSalmon/Flowberta
de72e75266e7ef4159d6332980a3cae772942c92
2021-06-28T19:13:14.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
BigSalmon
null
BigSalmon/Flowberta
16
null
transformers
9,157
Entry not found
BigSalmon/MrLincolnBerta
3e92eea4fdc7a93fbd5cf93ee75643ffa6bd7b17
2021-12-24T21:54:31.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
BigSalmon
null
BigSalmon/MrLincolnBerta
16
null
transformers
9,158
Example Prompt: ``` informal english: things are better when they are open source, because they are constantly being updated to enhance experience. Translated into the Style of Abraham Lincoln: in the open-source paradigm, code is ( ceaselessly / perpetually ) being ( reengineered / revamped / polished ), thereby ( advancing / enhancing / optimizing / <mask> ) the user experience. ``` Demo: https://huggingface.co/spaces/BigSalmon/MASK2
Brian-M-Collins/article-twitter-summarisation-1
641a3204fdc60f4eb92215514445f8a85f2443c2
2022-02-22T16:37:46.000Z
[ "pytorch", "pegasus", "text2text-generation", "unk", "dataset:Brian-M-Collins/autonlp-data-abstract-twitter-summarisation", "transformers", "autonlp", "co2_eq_emissions", "autotrain_compatible" ]
text2text-generation
false
Brian-M-Collins
null
Brian-M-Collins/article-twitter-summarisation-1
16
1
transformers
9,159
--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - Brian-M-Collins/autonlp-data-abstract-twitter-summarisation co2_eq_emissions: 122.0276344811897 --- # Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 588216503 - CO2 Emissions (in grams): 122.0276344811897 ## Validation Metrics - Loss: 1.2534778118133545 - Rouge1: 67.1992 - Rouge2: 58.3369 - RougeL: 64.0987 - RougeLsum: 65.2118 - Gen Len: 37.4152 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/Brian-M-Collins/autonlp-abstract-twitter-summarisation-588216503 ```
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf
c97c040fdfe8c39453d6425d0dbe7c675161de1e
2021-10-18T09:58:40.000Z
[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]
token-classification
false
CAMeL-Lab
null
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf
16
null
transformers
9,160
--- language: - ar license: apache-2.0 widget: - text: 'شلونك ؟ شخبارك ؟' --- # CAMeLBERT-DA POS-GLF Model ## Model description **CAMeLBERT-DA POS-GLF Model** is a Gulf Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-DA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model. For the fine-tuning, we used the [Gumar](https://camel.abudhabi.nyu.edu/annotated-gumar-corpus/) dataset. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT). ## Intended uses You can use the CAMeLBERT-DA POS-GLF model as part of the transformers pipeline. This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon. #### How to use To use the model with a transformers pipeline: ```python >>> from transformers import pipeline >>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf') >>> text = 'شلونك ؟ شخبارك ؟' >>> pos(text) [{'entity': 'noun', 'score': 0.84596395, 'index': 1, 'word': 'شلون', 'start': 0, 'end': 4}, {'entity': 'prep', 'score': 0.7230489, 'index': 2, 'word': '##ك', 'start': 4, 'end': 5}, {'entity': 'punc', 'score': 0.99996364, 'index': 3, 'word': '؟', 'start': 6, 'end': 7}, {'entity': 'noun', 'score': 0.9990874, 'index': 4, 'word': 'ش', 'start': 8, 'end': 9}, {'entity': 'noun', 'score': 0.99985224, 'index': 5, 'word': '##خبار', 'start': 9, 'end': 13}, {'entity': 'noun', 'score': 0.9988868, 'index': 6, 'word': '##ك', 'start': 13, 'end': 14}, {'entity': 'punc', 'score': 0.9999683, 'index': 7, 'word': '؟', 'start': 15, 'end': 16}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. ## Citation ```bibtex @inproceedings{inoue-etal-2021-interplay, title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models", author = "Inoue, Go and Alhafni, Bashar and Baimukan, Nurpeiis and Bouamor, Houda and Habash, Nizar", booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", month = apr, year = "2021", address = "Kyiv, Ukraine (Online)", publisher = "Association for Computational Linguistics", abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.", } ```
CZWin32768/xlm-align
6b7a0812d30b3fe2c78fc49d91aa234d969b7bc7
2021-07-21T07:53:29.000Z
[ "pytorch", "xlm-roberta", "fill-mask", "arxiv:2106.06381", "transformers", "autotrain_compatible" ]
fill-mask
false
CZWin32768
null
CZWin32768/xlm-align
16
null
transformers
9,161
# XLM-Align **Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment** (ACL-2021, [paper](https://arxiv.org/pdf/2106.06381.pdf), [github](https://github.com/CZWin32768/XLM-Align)) XLM-Align is a pretrained cross-lingual language model that supports 94 languages. See details in our [paper](https://arxiv.org/pdf/2106.06381.pdf). ## Example ``` model = = AutoModel.from_pretrained("CZWin32768/xlm-align") ``` ## Evaluation Results XTREME cross-lingual understanding tasks: | Model | POS | NER | XQuAD | MLQA | TyDiQA | XNLI | PAWS-X | Avg | |:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| | XLM-R_base | 75.6 | 61.8 | 71.9 / 56.4 | 65.1 / 47.2 | 55.4 / 38.3 | 75.0 | 84.9 | 66.4 | | XLM-Align | **76.0** | **63.7** | **74.7 / 59.0** | **68.1 / 49.8** | **62.1 / 44.8** | **76.2** | **86.8** | **68.9** | ## MD5 ``` b9d214025837250ede2f69c9385f812c config.json 6005db708eb4bab5b85fa3976b9db85b pytorch_model.bin bf25eb5120ad92ef5c7d8596b5dc4046 sentencepiece.bpe.model eedbd60a7268b9fc45981b849664f747 tokenizer.json ``` ## About Contact: chizewen\@outlook.com BibTeX: ``` @article{xlmalign, title={Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment}, author={Zewen Chi and Li Dong and Bo Zheng and Shaohan Huang and Xian-Ling Mao and Heyan Huang and Furu Wei}, journal={arXiv preprint arXiv:2106.06381}, year={2021} } ```
DataikuNLP/paraphrase-albert-small-v2
94e7d607209b5e81aedd815066e084d7b1e227cd
2021-09-01T13:30:27.000Z
[ "pytorch", "albert", "feature-extraction", "arxiv:1908.10084", "sentence-transformers", "sentence-similarity", "transformers", "license:apache-2.0" ]
sentence-similarity
false
DataikuNLP
null
DataikuNLP/paraphrase-albert-small-v2
16
null
sentence-transformers
9,162
--- pipeline_tag: sentence-similarity license: apache-2.0 tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # DataikuNLP/paraphrase-albert-small-v2 **This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/paraphrase-albert-small-v2/) from sentence-transformers at the specific commit `1eb1996223dd90a4c25be2fc52f6f336419a0d52`.** This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/paraphrase-albert-small-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-albert-small-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-albert-small-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-albert-small-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 100, 'do_lower_case': False}) with Transformer model: AlbertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```
Davlan/xlm-roberta-base-finetuned-igbo
0c10763e2cf85e0e393ea7a9ccf1365ff9dcc0c9
2021-06-06T20:13:58.000Z
[ "pytorch", "xlm-roberta", "fill-mask", "ig", "transformers", "autotrain_compatible" ]
fill-mask
false
Davlan
null
Davlan/xlm-roberta-base-finetuned-igbo
16
null
transformers
9,163
Hugging Face's logo --- language: ig datasets: --- # xlm-roberta-base-finetuned-igbo ## Model description **xlm-roberta-base-finetuned-igbo** is a **Igbo RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Hausa language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Igbo corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-igbo') >>> unmasker("Reno Omokri na Gọọmentị <mask> enweghị ihe ha ga-eji hiwe ya bụ mmachi.") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 + OPUS CC-Align + [IGBO NLP Corpus](https://github.com/IgnatiusEzeani/IGBONLP) +[Igbo CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| XLM-R F1 | ig_roberta F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 84.51 | 87.74 ### BibTeX entry and citation info By David Adelani ``` ```
Davlan/xlm-roberta-base-finetuned-kinyarwanda
eb8a4a3573047b37b6be993c7b04c6374ca30fd6
2021-06-15T20:24:02.000Z
[ "pytorch", "xlm-roberta", "fill-mask", "rw", "transformers", "autotrain_compatible" ]
fill-mask
false
Davlan
null
Davlan/xlm-roberta-base-finetuned-kinyarwanda
16
null
transformers
9,164
Hugging Face's logo --- language: rw datasets: --- # xlm-roberta-base-finetuned-kinyarwanda ## Model description **xlm-roberta-base-finetuned-kinyarwanda** is a **Kinyarwanda RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Kinyarwanda language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Kinyarwanda corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-kinyarwanda') >>> unmasker("Twabonye ko igihe mu <mask> hazaba hari ikirango abantu bakunze") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 + [KIRNEWS](https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus) + [BBC Gahuza](https://www.bbc.com/gahuza) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| XLM-R F1 | rw_roberta F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 73.22 | 77.76 ### BibTeX entry and citation info By David Adelani ``` ```
EhsanAghazadeh/xlnet-large-cased-CoLA_B
a5d146f1c4c291db0758e1c287ff7242c3dff7cc
2021-04-19T10:59:46.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
false
EhsanAghazadeh
null
EhsanAghazadeh/xlnet-large-cased-CoLA_B
16
null
transformers
9,165
Entry not found
FFZG-cleopatra/bert-emoji-latvian-twitter
42b35cea36e13ed9662840dcd9ecaa8db7595cc0
2021-05-18T18:33:26.000Z
[ "pytorch", "jax", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
FFZG-cleopatra
null
FFZG-cleopatra/bert-emoji-latvian-twitter
16
null
transformers
9,166
Entry not found
FremyCompany/xls-r-2b-nl-v2_lm-5gram-os2_hunspell
27c2d35249d3976ee0b4d15b0eda65025dd18b70
2022-03-23T18:28:16.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "nl", "dataset:mozilla-foundation/common_voice_8_0", "transformers", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "nl_BE", "nl_NL", "robust-speech-event", "model-index" ]
automatic-speech-recognition
false
FremyCompany
null
FremyCompany/xls-r-2b-nl-v2_lm-5gram-os2_hunspell
16
1
transformers
9,167
--- language: - nl tags: - automatic-speech-recognition - hf-asr-leaderboard - model_for_talk - mozilla-foundation/common_voice_8_0 - nl - nl_BE - nl_NL - robust-speech-event datasets: - mozilla-foundation/common_voice_8_0 model-index: - name: xls-r-nl-v1-cv8-lm results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: nl metrics: - name: Test WER type: wer value: 3.93 - name: Test CER type: cer value: 1.22 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: nl metrics: - name: Test WER type: wer value: 16.35 - name: Test CER type: cer value: 9.64 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: nl metrics: - name: Test WER type: wer value: 15.81 --- # XLS-R-based CTC model with 5-gram language model from Open Subtitles This model is a version of [facebook/wav2vec2-xls-r-2b-22-to-16](https://huggingface.co/facebook/wav2vec2-xls-r-2b-22-to-16) fine-tuned mainly on the [CGN dataset](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-nederlands/), as well as the [MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL](https://commonvoice.mozilla.org) dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0): - Wer: 0.03931 - Cer: 0.01224 > **IMPORTANT NOTE**: The `hunspell` typo fixer is **not enabled** on the website, which returns raw CTC+LM results. Hunspell reranking is only available in the `eval.py` decoding script. For best results, please use the code in that file while using the model locally for inference. > **IMPORTANT NOTE**: Evaluating this model requires `apt install libhunspell-dev` and a pip install of `hunspell` in addition to pip installs of `pipy-kenlm` and `pyctcdecode` (see `install_requirements.sh`); in addition, the chunking lengths and strides were optimized for the model as `12s` and `2s` respectively (see `eval.sh`). > **QUICK REMARK**: The "Robust Speech Event" set does not contain cleaned transcription text, so its WER/CER are vastly over-estimated. For instance `2014` in the dev set is left as a number but will be recognized as `tweeduizend veertien`, which counts as 3 mistakes (`2014` missing, and both `tweeduizend` and `veertien` wrongly inserted). Other normalization problems in the dev set include the presence of single quotes around some words, that then end up as non-match despite being the correct word (but without quotes), and the removal of some speech words in the final transcript (`ja`, etc...). As a result, our real error rate on the dev set is significantly lower than reported. > > ![Image showing the difference between the prediction and target of the dev set](https://huggingface.co/FremyCompany/xls-r-2b-nl-v2_lm-5gram-os2_hunspell/resolve/main/dev_set_diff_4.png) > > You can compare the [predictions](https://huggingface.co/FremyCompany/xls-r-2b-nl-v2_lm-5gram-os2_hunspell/blob/main/log_speech-recognition-community-v2_dev_data_nl_validation_predictions.txt) with the [targets](https://huggingface.co/FremyCompany/xls-r-2b-nl-v2_lm-5gram-os2_hunspell/blob/main/log_speech-recognition-community-v2_dev_data_nl_validation_targets.txt) on the validation dev set yourself, for example using [this diffing tool](https://countwordsfree.com/comparetexts). > **WE DO SPEECH RECOGNITION**: Hello reader! If you are considering using this (or another) model in production, but would benefit from a model fine-tuned specifically for your use case (using text and/or labelled speech), feel free to [contact our team](https://www.ugent.be/ea/idlab/en/research/semantic-intelligence/speech-and-audio-processing.htm). This model was developped during the [Robust Speech Recognition challenge](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) event by [François REMY](https://www.linkedin.com/in/fremycompany/) [(twitter)](https://twitter.com/FremyCompany) and [Geoffroy VANDERREYDT](https://be.linkedin.com/in/geoffroy-vanderreydt-a4421460). > We would like to thank [OVH](https://www.ovhcloud.com/en/public-cloud/ai-training/) for providing us with a V100S GPU. ## Model description The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. To improve accuracy, a beam-search decoder based on `pyctcdecode` is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus. To further deal with typos, `hunspell` is used to propose alternative spellings for words not in the unigrams of the language model. These alternatives are then reranked based on the language model trained above, and a penalty proportional to the levenshtein edit distance between the alternative and the recognized word. This for examples enables to correct `collegas` into `collega's` or `gogol` into `google`. ## Intended uses & limitations This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation). ## Training and evaluation data The model was: 0. initialized with [the 2B parameter model from Facebook](facebook/wav2vec2-xls-r-2b-22-to-16). 1. trained `5` epochs (6000 iterations of batch size 32) on [the `cv8/nl` dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0). 2. trained `1` epoch (36000 iterations of batch size 32) on [the `cgn` dataset](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-nederlands/). 3. trained `5` epochs (6000 iterations of batch size 32) on [the `cv8/nl` dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0). ### Framework versions - Transformers 4.16.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0
GD/cq-bert-model-repo
6058c3537d6dc0ea8305100de6d7df5aa8f78f02
2021-05-18T18:40:31.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
GD
null
GD/cq-bert-model-repo
16
null
transformers
9,168
Entry not found
Geotrend/bert-base-sw-cased
7d9ca957a81d2449cf1319af0b91f75f11642336
2021-05-18T20:10:30.000Z
[ "pytorch", "tf", "jax", "bert", "fill-mask", "sw", "dataset:wikipedia", "transformers", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
Geotrend
null
Geotrend/bert-base-sw-cased
16
null
transformers
9,169
--- language: sw datasets: wikipedia license: apache-2.0 --- # bert-base-sw-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-sw-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-sw-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact [email protected] for any question, feedback or request.
Helsinki-NLP/opus-mt-cau-en
593400efa7e1ae4f6cf96ed9ff1d524099a47ad5
2021-01-18T07:53:27.000Z
[ "pytorch", "marian", "text2text-generation", "ab", "ka", "ce", "cau", "en", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-cau-en
16
null
transformers
9,170
--- language: - ab - ka - ce - cau - en tags: - translation license: apache-2.0 --- ### cau-eng * source group: Caucasian languages * target group: English * OPUS readme: [cau-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cau-eng/README.md) * model: transformer * source language(s): abk ady che kat * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus2m-2020-07-31.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/cau-eng/opus2m-2020-07-31.zip) * test set translations: [opus2m-2020-07-31.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cau-eng/opus2m-2020-07-31.test.txt) * test set scores: [opus2m-2020-07-31.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cau-eng/opus2m-2020-07-31.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.abk-eng.abk.eng | 0.3 | 0.134 | | Tatoeba-test.ady-eng.ady.eng | 0.4 | 0.104 | | Tatoeba-test.che-eng.che.eng | 0.6 | 0.128 | | Tatoeba-test.kat-eng.kat.eng | 18.6 | 0.366 | | Tatoeba-test.multi.eng | 16.6 | 0.351 | ### System Info: - hf_name: cau-eng - source_languages: cau - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cau-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ab', 'ka', 'ce', 'cau', 'en'] - src_constituents: {'abk', 'kat', 'che', 'ady'} - tgt_constituents: {'eng'} - src_multilingual: True - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/cau-eng/opus2m-2020-07-31.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/cau-eng/opus2m-2020-07-31.test.txt - src_alpha3: cau - tgt_alpha3: eng - short_pair: cau-en - chrF2_score: 0.35100000000000003 - bleu: 16.6 - brevity_penalty: 1.0 - ref_len: 6285.0 - src_name: Caucasian languages - tgt_name: English - train_date: 2020-07-31 - src_alpha2: cau - tgt_alpha2: en - prefer_old: False - long_pair: cau-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-de-el
ad3da773c26cf72780d46b4a75333226a19760e4
2021-09-09T21:30:47.000Z
[ "pytorch", "marian", "text2text-generation", "de", "el", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-de-el
16
null
transformers
9,171
--- tags: - translation license: apache-2.0 --- ### opus-mt-de-el * source languages: de * target languages: el * OPUS readme: [de-el](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-el/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-el/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-el/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-el/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.de.el | 45.7 | 0.649 |
Helsinki-NLP/opus-mt-en-cel
d1a4b332d5c90e1651d1e280dfb3a80b7ea07059
2021-01-18T08:05:58.000Z
[ "pytorch", "marian", "text2text-generation", "en", "gd", "ga", "br", "kw", "gv", "cy", "cel", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-en-cel
16
null
transformers
9,172
--- language: - en - gd - ga - br - kw - gv - cy - cel tags: - translation license: apache-2.0 --- ### eng-cel * source group: English * target group: Celtic languages * OPUS readme: [eng-cel](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cel/README.md) * model: transformer * source language(s): eng * target language(s): bre cor cym gla gle glv * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cel/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cel/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cel/opus2m-2020-08-01.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng-bre.eng.bre | 11.5 | 0.338 | | Tatoeba-test.eng-cor.eng.cor | 0.3 | 0.095 | | Tatoeba-test.eng-cym.eng.cym | 31.0 | 0.549 | | Tatoeba-test.eng-gla.eng.gla | 7.6 | 0.317 | | Tatoeba-test.eng-gle.eng.gle | 35.9 | 0.582 | | Tatoeba-test.eng-glv.eng.glv | 9.9 | 0.454 | | Tatoeba-test.eng.multi | 18.0 | 0.342 | ### System Info: - hf_name: eng-cel - source_languages: eng - target_languages: cel - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cel/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'gd', 'ga', 'br', 'kw', 'gv', 'cy', 'cel'] - src_constituents: {'eng'} - tgt_constituents: {'gla', 'gle', 'bre', 'cor', 'glv', 'cym'} - src_multilingual: False - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cel/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cel/opus2m-2020-08-01.test.txt - src_alpha3: eng - tgt_alpha3: cel - short_pair: en-cel - chrF2_score: 0.342 - bleu: 18.0 - brevity_penalty: 0.9590000000000001 - ref_len: 45370.0 - src_name: English - tgt_name: Celtic languages - train_date: 2020-08-01 - src_alpha2: en - tgt_alpha2: cel - prefer_old: False - long_pair: eng-cel - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-en-iso
8c635a5689d7815ecc3453d48e4dee1e367b60ad
2021-09-09T21:36:23.000Z
[ "pytorch", "marian", "text2text-generation", "en", "iso", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-en-iso
16
null
transformers
9,173
--- tags: - translation license: apache-2.0 --- ### opus-mt-en-iso * source languages: en * target languages: iso * OPUS readme: [en-iso](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-iso/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-iso/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-iso/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-iso/opus-2020-01-08.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.en.iso | 35.7 | 0.523 |
Helsinki-NLP/opus-mt-en-lun
43db264508a6c6cd238433c9ba3b37d00e40d8b0
2021-09-09T21:37:16.000Z
[ "pytorch", "marian", "text2text-generation", "en", "lun", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-en-lun
16
null
transformers
9,174
--- tags: - translation license: apache-2.0 --- ### opus-mt-en-lun * source languages: en * target languages: lun * OPUS readme: [en-lun](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-lun/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-lun/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-lun/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-lun/opus-2020-01-08.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.en.lun | 28.9 | 0.552 |
Helsinki-NLP/opus-mt-en-rnd
28b5064598c0673a1c090c9e5f2a0263d112a6dd
2021-09-09T21:38:40.000Z
[ "pytorch", "marian", "text2text-generation", "en", "rnd", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-en-rnd
16
null
transformers
9,175
--- tags: - translation license: apache-2.0 --- ### opus-mt-en-rnd * source languages: en * target languages: rnd * OPUS readme: [en-rnd](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-rnd/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-rnd/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-rnd/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-rnd/opus-2020-01-08.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.en.rnd | 34.5 | 0.571 |
Helsinki-NLP/opus-mt-es-bg
7ae5fe6ec5bf8418cb17265f64a8311706679320
2021-01-18T08:22:14.000Z
[ "pytorch", "marian", "text2text-generation", "es", "bg", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-es-bg
16
null
transformers
9,176
--- language: - es - bg tags: - translation license: apache-2.0 --- ### spa-bul * source group: Spanish * target group: Bulgarian * OPUS readme: [spa-bul](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-bul/README.md) * model: transformer * source language(s): spa * target language(s): bul * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-bul/opus-2020-07-03.zip) * test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-bul/opus-2020-07-03.test.txt) * test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-bul/opus-2020-07-03.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.spa.bul | 50.9 | 0.674 | ### System Info: - hf_name: spa-bul - source_languages: spa - target_languages: bul - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-bul/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['es', 'bg'] - src_constituents: {'spa'} - tgt_constituents: {'bul', 'bul_Latn'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-bul/opus-2020-07-03.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-bul/opus-2020-07-03.test.txt - src_alpha3: spa - tgt_alpha3: bul - short_pair: es-bg - chrF2_score: 0.674 - bleu: 50.9 - brevity_penalty: 0.955 - ref_len: 1707.0 - src_name: Spanish - tgt_name: Bulgarian - train_date: 2020-07-03 - src_alpha2: es - tgt_alpha2: bg - prefer_old: False - long_pair: spa-bul - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-es-fj
5e65adc44f3d16417b98961603e0e3e5a9289441
2021-09-09T21:42:23.000Z
[ "pytorch", "marian", "text2text-generation", "es", "fj", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-es-fj
16
null
transformers
9,177
--- tags: - translation license: apache-2.0 --- ### opus-mt-es-fj * source languages: es * target languages: fj * OPUS readme: [es-fj](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-fj/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-fj/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-fj/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-fj/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.es.fj | 24.8 | 0.472 |
Helsinki-NLP/opus-mt-es-gil
6830b9bb1c10b0aeddda5a868c1a80cbb51a5419
2021-09-09T21:42:34.000Z
[ "pytorch", "marian", "text2text-generation", "es", "gil", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-es-gil
16
null
transformers
9,178
--- tags: - translation license: apache-2.0 --- ### opus-mt-es-gil * source languages: es * target languages: gil * OPUS readme: [es-gil](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-gil/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-gil/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-gil/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-gil/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.es.gil | 23.8 | 0.470 |
Helsinki-NLP/opus-mt-es-iso
2be50239f12c54423830965a7b6d21224a732502
2021-09-09T21:43:13.000Z
[ "pytorch", "marian", "text2text-generation", "es", "iso", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-es-iso
16
null
transformers
9,179
--- tags: - translation license: apache-2.0 --- ### opus-mt-es-iso * source languages: es * target languages: iso * OPUS readme: [es-iso](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-iso/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-iso/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-iso/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-iso/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.es.iso | 22.4 | 0.396 |
Helsinki-NLP/opus-mt-es-xh
e3d7a2282fb56f5e67f544b663b8b0c99ed5bdab
2021-09-09T21:45:42.000Z
[ "pytorch", "marian", "text2text-generation", "es", "xh", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-es-xh
16
null
transformers
9,180
--- tags: - translation license: apache-2.0 --- ### opus-mt-es-xh * source languages: es * target languages: xh * OPUS readme: [es-xh](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-xh/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-xh/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-xh/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-xh/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.es.xh | 25.0 | 0.541 |
Helsinki-NLP/opus-mt-euq-en
998d513f5e3badcdb36381d4b161987e95b571b7
2021-01-18T08:31:22.000Z
[ "pytorch", "marian", "text2text-generation", "euq", "en", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-euq-en
16
null
transformers
9,181
--- language: - euq - en tags: - translation license: apache-2.0 --- ### euq-eng * source group: Basque (family) * target group: English * OPUS readme: [euq-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/euq-eng/README.md) * model: transformer * source language(s): eus * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (spm12k,spm12k) * download original weights: [opus-2020-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/euq-eng/opus-2020-07-26.zip) * test set translations: [opus-2020-07-26.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/euq-eng/opus-2020-07-26.test.txt) * test set scores: [opus-2020-07-26.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/euq-eng/opus-2020-07-26.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eus.eng | 41.5 | 0.594 | | Tatoeba-test.eus-eng.eus.eng | 41.5 | 0.594 | ### System Info: - hf_name: euq-eng - source_languages: euq - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/euq-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['euq', 'en'] - src_constituents: {'eus'} - tgt_constituents: {'eng'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm12k,spm12k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/euq-eng/opus-2020-07-26.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/euq-eng/opus-2020-07-26.test.txt - src_alpha3: euq - tgt_alpha3: eng - short_pair: euq-en - chrF2_score: 0.594 - bleu: 41.5 - brevity_penalty: 0.9640000000000001 - ref_len: 8157.0 - src_name: Basque (family) - tgt_name: English - train_date: 2020-07-26 - src_alpha2: euq - tgt_alpha2: en - prefer_old: False - long_pair: euq-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-fi-ru
d34952308f6510507b39b81818e07ab83c7d4f4d
2021-09-09T21:50:24.000Z
[ "pytorch", "marian", "text2text-generation", "fi", "ru", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-fi-ru
16
null
transformers
9,182
--- tags: - translation license: apache-2.0 --- ### opus-mt-fi-ru * source languages: fi * target languages: ru * OPUS readme: [fi-ru](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-ru/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-04-12.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-ru/opus-2020-04-12.zip) * test set translations: [opus-2020-04-12.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-ru/opus-2020-04-12.test.txt) * test set scores: [opus-2020-04-12.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-ru/opus-2020-04-12.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.fi.ru | 46.3 | 0.670 |
Helsinki-NLP/opus-mt-fiu-fiu
e97528e540d0b0527b643c9fea7da165ea79f044
2021-01-18T08:41:00.000Z
[ "pytorch", "marian", "text2text-generation", "se", "fi", "hu", "et", "fiu", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-fiu-fiu
16
null
transformers
9,183
--- language: - se - fi - hu - et - fiu tags: - translation license: apache-2.0 --- ### fiu-fiu * source group: Finno-Ugrian languages * target group: Finno-Ugrian languages * OPUS readme: [fiu-fiu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fiu-fiu/README.md) * model: transformer * source language(s): est fin fkv_Latn hun izh krl liv_Latn vep vro * target language(s): est fin fkv_Latn hun izh krl liv_Latn vep vro * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fiu-fiu/opus-2020-07-26.zip) * test set translations: [opus-2020-07-26.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fiu-fiu/opus-2020-07-26.test.txt) * test set scores: [opus-2020-07-26.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fiu-fiu/opus-2020-07-26.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.est-est.est.est | 2.0 | 0.252 | | Tatoeba-test.est-fin.est.fin | 51.0 | 0.704 | | Tatoeba-test.est-fkv.est.fkv | 1.1 | 0.211 | | Tatoeba-test.est-vep.est.vep | 3.1 | 0.272 | | Tatoeba-test.fin-est.fin.est | 55.2 | 0.722 | | Tatoeba-test.fin-fkv.fin.fkv | 1.6 | 0.207 | | Tatoeba-test.fin-hun.fin.hun | 42.4 | 0.663 | | Tatoeba-test.fin-izh.fin.izh | 12.9 | 0.509 | | Tatoeba-test.fin-krl.fin.krl | 4.6 | 0.292 | | Tatoeba-test.fkv-est.fkv.est | 2.4 | 0.148 | | Tatoeba-test.fkv-fin.fkv.fin | 15.1 | 0.427 | | Tatoeba-test.fkv-liv.fkv.liv | 1.2 | 0.261 | | Tatoeba-test.fkv-vep.fkv.vep | 1.2 | 0.233 | | Tatoeba-test.hun-fin.hun.fin | 47.8 | 0.681 | | Tatoeba-test.izh-fin.izh.fin | 24.0 | 0.615 | | Tatoeba-test.izh-krl.izh.krl | 1.8 | 0.114 | | Tatoeba-test.krl-fin.krl.fin | 13.6 | 0.407 | | Tatoeba-test.krl-izh.krl.izh | 2.7 | 0.096 | | Tatoeba-test.liv-fkv.liv.fkv | 1.2 | 0.164 | | Tatoeba-test.liv-vep.liv.vep | 3.4 | 0.181 | | Tatoeba-test.multi.multi | 36.7 | 0.581 | | Tatoeba-test.vep-est.vep.est | 3.4 | 0.251 | | Tatoeba-test.vep-fkv.vep.fkv | 1.2 | 0.215 | | Tatoeba-test.vep-liv.vep.liv | 3.4 | 0.179 | ### System Info: - hf_name: fiu-fiu - source_languages: fiu - target_languages: fiu - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fiu-fiu/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['se', 'fi', 'hu', 'et', 'fiu'] - src_constituents: {'izh', 'mdf', 'vep', 'vro', 'sme', 'myv', 'fkv_Latn', 'krl', 'fin', 'hun', 'kpv', 'udm', 'liv_Latn', 'est', 'mhr', 'sma'} - tgt_constituents: {'izh', 'mdf', 'vep', 'vro', 'sme', 'myv', 'fkv_Latn', 'krl', 'fin', 'hun', 'kpv', 'udm', 'liv_Latn', 'est', 'mhr', 'sma'} - src_multilingual: True - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/fiu-fiu/opus-2020-07-26.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/fiu-fiu/opus-2020-07-26.test.txt - src_alpha3: fiu - tgt_alpha3: fiu - short_pair: fiu-fiu - chrF2_score: 0.581 - bleu: 36.7 - brevity_penalty: 0.981 - ref_len: 19444.0 - src_name: Finno-Ugrian languages - tgt_name: Finno-Ugrian languages - train_date: 2020-07-26 - src_alpha2: fiu - tgt_alpha2: fiu - prefer_old: False - long_pair: fiu-fiu - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-fr-bcl
ce60f394625acd7cc59cbdcf1766ae5d7d698ecf
2021-09-09T21:52:53.000Z
[ "pytorch", "marian", "text2text-generation", "fr", "bcl", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-fr-bcl
16
null
transformers
9,184
--- tags: - translation license: apache-2.0 --- ### opus-mt-fr-bcl * source languages: fr * target languages: bcl * OPUS readme: [fr-bcl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-bcl/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-bcl/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-bcl/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-bcl/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.fr.bcl | 35.9 | 0.566 |
Helsinki-NLP/opus-mt-ilo-fi
737504a377f6cbc1d343dabb08fc8ee4a51c0ee2
2021-09-09T22:12:01.000Z
[ "pytorch", "marian", "text2text-generation", "ilo", "fi", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-ilo-fi
16
null
transformers
9,185
--- tags: - translation license: apache-2.0 --- ### opus-mt-ilo-fi * source languages: ilo * target languages: fi * OPUS readme: [ilo-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ilo-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/ilo-fi/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ilo-fi/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ilo-fi/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.ilo.fi | 27.7 | 0.516 |
Helsinki-NLP/opus-mt-kab-en
a35e4ac154b973afd28e971dbaabdea228a7da47
2021-09-10T13:53:34.000Z
[ "pytorch", "marian", "text2text-generation", "kab", "en", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-kab-en
16
null
transformers
9,186
--- tags: - translation license: apache-2.0 --- ### opus-mt-kab-en * source languages: kab * target languages: en * OPUS readme: [kab-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/kab-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/kab-en/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/kab-en/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/kab-en/opus-2019-12-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.kab.en | 27.5 | 0.408 |
Helsinki-NLP/opus-mt-kwn-en
1cb7ac7558ae7f2c7bedafe47d8b46572aeef605
2021-09-10T13:54:24.000Z
[ "pytorch", "marian", "text2text-generation", "kwn", "en", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-kwn-en
16
null
transformers
9,187
--- tags: - translation license: apache-2.0 --- ### opus-mt-kwn-en * source languages: kwn * target languages: en * OPUS readme: [kwn-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/kwn-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/kwn-en/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/kwn-en/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/kwn-en/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.kwn.en | 27.5 | 0.434 |
Helsinki-NLP/opus-mt-nic-en
9127f7835c52a5389bd8e6c5f27dd44f6b010230
2020-08-21T14:42:48.000Z
[ "pytorch", "marian", "text2text-generation", "sn", "rw", "wo", "ig", "sg", "ee", "zu", "lg", "ts", "ln", "ny", "yo", "rn", "xh", "nic", "en", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-nic-en
16
null
transformers
9,188
--- language: - sn - rw - wo - ig - sg - ee - zu - lg - ts - ln - ny - yo - rn - xh - nic - en tags: - translation license: apache-2.0 --- ### nic-eng * source group: Niger-Kordofanian languages * target group: English * OPUS readme: [nic-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nic-eng/README.md) * model: transformer * source language(s): bam_Latn ewe fuc fuv ibo kin lin lug nya run sag sna swh toi_Latn tso umb wol xho yor zul * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nic-eng/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nic-eng/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nic-eng/opus2m-2020-08-01.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.bam-eng.bam.eng | 2.4 | 0.090 | | Tatoeba-test.ewe-eng.ewe.eng | 10.3 | 0.384 | | Tatoeba-test.ful-eng.ful.eng | 1.2 | 0.114 | | Tatoeba-test.ibo-eng.ibo.eng | 7.5 | 0.197 | | Tatoeba-test.kin-eng.kin.eng | 30.7 | 0.481 | | Tatoeba-test.lin-eng.lin.eng | 3.1 | 0.185 | | Tatoeba-test.lug-eng.lug.eng | 3.1 | 0.261 | | Tatoeba-test.multi.eng | 21.3 | 0.377 | | Tatoeba-test.nya-eng.nya.eng | 31.6 | 0.502 | | Tatoeba-test.run-eng.run.eng | 24.9 | 0.420 | | Tatoeba-test.sag-eng.sag.eng | 5.2 | 0.231 | | Tatoeba-test.sna-eng.sna.eng | 20.1 | 0.374 | | Tatoeba-test.swa-eng.swa.eng | 4.6 | 0.191 | | Tatoeba-test.toi-eng.toi.eng | 4.8 | 0.122 | | Tatoeba-test.tso-eng.tso.eng | 100.0 | 1.000 | | Tatoeba-test.umb-eng.umb.eng | 9.0 | 0.246 | | Tatoeba-test.wol-eng.wol.eng | 14.0 | 0.212 | | Tatoeba-test.xho-eng.xho.eng | 38.2 | 0.558 | | Tatoeba-test.yor-eng.yor.eng | 21.2 | 0.364 | | Tatoeba-test.zul-eng.zul.eng | 42.3 | 0.589 | ### System Info: - hf_name: nic-eng - source_languages: nic - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nic-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['sn', 'rw', 'wo', 'ig', 'sg', 'ee', 'zu', 'lg', 'ts', 'ln', 'ny', 'yo', 'rn', 'xh', 'nic', 'en'] - src_constituents: {'bam_Latn', 'sna', 'kin', 'wol', 'ibo', 'swh', 'sag', 'ewe', 'zul', 'fuc', 'lug', 'tso', 'lin', 'nya', 'yor', 'run', 'xho', 'fuv', 'toi_Latn', 'umb'} - tgt_constituents: {'eng'} - src_multilingual: True - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nic-eng/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nic-eng/opus2m-2020-08-01.test.txt - src_alpha3: nic - tgt_alpha3: eng - short_pair: nic-en - chrF2_score: 0.377 - bleu: 21.3 - brevity_penalty: 1.0 - ref_len: 15228.0 - src_name: Niger-Kordofanian languages - tgt_name: English - train_date: 2020-08-01 - src_alpha2: nic - tgt_alpha2: en - prefer_old: False - long_pair: nic-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-sv-nl
943288a8d7959a1bb49be2d7a288ef1af0d4abe4
2021-09-10T14:08:29.000Z
[ "pytorch", "marian", "text2text-generation", "sv", "nl", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-sv-nl
16
null
transformers
9,189
--- tags: - translation license: apache-2.0 --- ### opus-mt-sv-nl * source languages: sv * target languages: nl * OPUS readme: [sv-nl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-nl/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-nl/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-nl/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-nl/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | GlobalVoices.sv.nl | 24.3 | 0.522 |
Helsinki-NLP/opus-mt-tvl-en
f9a9b5dc37025ce839d91c066c3e5198cbcf5747
2021-09-11T10:50:22.000Z
[ "pytorch", "marian", "text2text-generation", "tvl", "en", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-tvl-en
16
null
transformers
9,190
--- tags: - translation license: apache-2.0 --- ### opus-mt-tvl-en * source languages: tvl * target languages: en * OPUS readme: [tvl-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tvl-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/tvl-en/opus-2020-01-21.zip) * test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/tvl-en/opus-2020-01-21.test.txt) * test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/tvl-en/opus-2020-01-21.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.tvl.en | 37.3 | 0.528 |
Helsinki-NLP/opus-mt-ve-en
35d134d0489f1503333835c46c10f7f724515c50
2021-09-11T10:51:37.000Z
[ "pytorch", "marian", "text2text-generation", "ve", "en", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-ve-en
16
1
transformers
9,191
--- tags: - translation license: apache-2.0 --- ### opus-mt-ve-en * source languages: ve * target languages: en * OPUS readme: [ve-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ve-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/ve-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ve-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ve-en/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.ve.en | 41.3 | 0.566 |
Helsinki-NLP/opus-mt-zh-sv
96cdf47752b6449a806039ca62c9aa541913c575
2020-08-21T14:42:52.000Z
[ "pytorch", "marian", "text2text-generation", "zh", "sv", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-zh-sv
16
null
transformers
9,192
--- language: - zh - sv tags: - translation license: apache-2.0 --- ### zho-swe * source group: Chinese * target group: Swedish * OPUS readme: [zho-swe](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-swe/README.md) * model: transformer-align * source language(s): cmn cmn_Bopo cmn_Hani cmn_Latn * target language(s): swe * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-swe/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-swe/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-swe/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.zho.swe | 46.1 | 0.621 | ### System Info: - hf_name: zho-swe - source_languages: zho - target_languages: swe - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-swe/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['zh', 'sv'] - src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'} - tgt_constituents: {'swe'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-swe/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-swe/opus-2020-06-17.test.txt - src_alpha3: zho - tgt_alpha3: swe - short_pair: zh-sv - chrF2_score: 0.621 - bleu: 46.1 - brevity_penalty: 0.956 - ref_len: 6223.0 - src_name: Chinese - tgt_name: Swedish - train_date: 2020-06-17 - src_alpha2: zh - tgt_alpha2: sv - prefer_old: False - long_pair: zho-swe - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
KBLab/bert-base-swedish-cased-squad-experimental
80399822d04785fc66ab4e214159808204e1acf0
2021-05-18T21:21:57.000Z
[ "pytorch", "jax", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
KBLab
null
KBLab/bert-base-swedish-cased-squad-experimental
16
null
transformers
9,193
Entry not found
KoichiYasuoka/roberta-base-japanese-aozora
3e995c5d15bdcac6df150a943d8b5cd35c783b8e
2022-02-13T02:02:34.000Z
[ "pytorch", "roberta", "fill-mask", "ja", "transformers", "japanese", "masked-lm", "license:cc-by-sa-4.0", "autotrain_compatible" ]
fill-mask
false
KoichiYasuoka
null
KoichiYasuoka/roberta-base-japanese-aozora
16
null
transformers
9,194
--- language: - "ja" tags: - "japanese" - "masked-lm" license: "cc-by-sa-4.0" pipeline_tag: "fill-mask" mask_token: "[MASK]" widget: - text: "日本に着いたら[MASK]を訪ねなさい。" --- # roberta-base-japanese-aozora ## Model Description This is a RoBERTa model pre-trained on 青空文庫 texts with [Japanese-LUW-Tokenizer](https://github.com/KoichiYasuoka/Japanese-LUW-Tokenizer). You can fine-tune `roberta-base-japanese-aozora` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-japanese-luw-upos), dependency-parsing, and so on. ## How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-japanese-aozora") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-base-japanese-aozora") ``` ## Reference 安岡孝一: [Transformersと国語研長単位による日本語係り受け解析モデルの製作](http://id.nii.ac.jp/1001/00216223/), 情報処理学会研究報告, Vol.2022-CH-128, No.7 (2022年2月), pp.1-8.
Matthijsvanhof/bert-base-dutch-cased-finetuned-NER
dcbaef9c349ae1be1f5b533450b14cbbc33e120f
2021-11-27T22:13:13.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
token-classification
false
Matthijsvanhof
null
Matthijsvanhof/bert-base-dutch-cased-finetuned-NER
16
null
transformers
9,195
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-dutch-cased-finetuned-NER results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-dutch-cased-finetuned-NER This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1078 - Precision: 0.6129 - Recall: 0.6639 - F1: 0.6374 - Accuracy: 0.9688 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 267 | 0.1131 | 0.6090 | 0.6264 | 0.6176 | 0.9678 | | 0.1495 | 2.0 | 534 | 0.1078 | 0.6129 | 0.6639 | 0.6374 | 0.9688 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
NYTK/sentiment-hts5-hubert-hungarian
074fff77a6a871d46322192462eb7fba1144c0e3
2022-01-26T13:18:49.000Z
[ "pytorch", "bert", "text-classification", "hu", "transformers", "license:gpl" ]
text-classification
false
NYTK
null
NYTK/sentiment-hts5-hubert-hungarian
16
null
transformers
9,196
--- language: - hu tags: - text-classification license: gpl metrics: - accuracy widget: - text: "Jó reggelt! majd küldöm az élményhozókat :)." --- # Hungarian Sentence-level Sentiment Analysis model with huBERT For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-analysis) or [our demo site](https://juniper.nytud.hu/demo/nlp). - Pretrained model used: huBERT - Finetuned on Hungarian Twitter Sentiment (HTS) Corpus - Labels: 1, 2, 3, 4, 5 ## Limitations - max_seq_length = 128 ## Results | Model | HTS2 | HTS5 | | ------------- | ------------- | ------------- | | huBERT | 85.55 | **68.99** | | XLM-RoBERTa| 85.56 | 85.56 | ## Citation If you use this model, please cite the following paper: ``` @inproceedings {yang-bart, title = {Improving Performance of Sentence-level Sentiment Analysis with Data Augmentation Methods}, booktitle = {Proceedings of 12th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2021)}, year = {2021}, publisher = {IEEE}, address = {Online}, author = {{Laki, László and Yang, Zijian Győző}} pages = {417--422} } ```
PremalMatalia/roberta-base-best-squad2
15bc545a70ee587dc6930c3677c3420edf4fda1b
2021-08-04T18:54:35.000Z
[ "pytorch", "roberta", "question-answering", "dataset:squad_v2", "transformers", "autotrain_compatible" ]
question-answering
false
PremalMatalia
null
PremalMatalia/roberta-base-best-squad2
16
1
transformers
9,197
--- datasets: - squad_v2 --- # RoBERTa-base for QA ## Overview **Language model:** 'roberta-base' </br> **Language:** English </br> **Downstream-task:** Extractive QA </br> **Training data:** SQuAD 2.0 </br> **Eval data:** SQuAD 2.0 </br> **Code:** <TBD> </br> ## Env Information `transformers` version: 4.9.1 </br> Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic </br> Python version: 3.7.11 </br> PyTorch version (GPU?): 1.9.0+cu102 (False)</br> Tensorflow version (GPU?): 2.5.0 (False)</br> ## Hyperparameters ``` max_seq_len=386 doc_stride=128 n_best_size=20 max_answer_length=30 min_null_score=7.0 batch_size=8 n_epochs=6 base_LM_model = "roberta-base" learning_rate=1.5e-5 adam_epsilon=1e-5 adam_beta1=0.95 adam_beta2=0.999 warmup_steps=100 weight_decay=0.01 optimizer=AdamW lr_scheduler="polynomial" ``` ##### There is a special threshold value CLS_threshold=-3 used to more accurately identify no answers [Logic will be available in GitHub Repo [TBD] ## Performance ``` "exact": 81.192622 "f1": 83.95408 "total": 11873 "HasAns_exact": 74.190283 "HasAns_f1": 79.721119 "HasAns_total": 5928 "NoAns_exact": 88.174937 "NoAns_f1": 88.174937 "NoAns_total": 5945 ``` ## Usage ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "PremalMatalia/roberta-base-best-squad2" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Which name is also used to describe the Amazon rainforest in English?', 'context': 'The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain "Amazonas" in their names. The Amazon represents over half of the planet\'s remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species.' } res = nlp(QA_input) print(res) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Premal Matalia
SEBIS/code_trans_t5_small_code_documentation_generation_java
1e77edf2b3efbff4135e04f2d3a3157849b666d6
2021-06-23T10:00:26.000Z
[ "pytorch", "jax", "t5", "feature-extraction", "transformers", "summarization" ]
summarization
false
SEBIS
null
SEBIS/code_trans_t5_small_code_documentation_generation_java
16
null
transformers
9,198
--- tags: - summarization widget: - text: "public static < T , U > Function < T , U > castFunction ( Class < U > target ) { return new CastToClass < T , U > ( target ) ; }" --- # CodeTrans model for code documentation generation java Pretrained model on programming language java using the t5 small model architecture. It was first released in [this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java functions. ## Model description This CodeTrans model is based on the `t5-small` model. It has its own SentencePiece vocabulary model. It used single-task training on CodeSearchNet Corpus java dataset. ## Intended uses & limitations The model could be used to generate the description for the java function or be fine-tuned on other java code tasks. It can be used on unparsed and untokenized java code. However, if the java code is tokenized, the performance should be better. ### How to use Here is how to use this model to generate java function documentation using Transformers SummarizationPipeline: ```python from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline pipeline = SummarizationPipeline( model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_code_documentation_generation_java"), tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_code_documentation_generation_java", skip_special_tokens=True), device=0 ) tokenized_code = "public static < T , U > Function < T , U > castFunction ( Class < U > target ) { return new CastToClass < T , U > ( target ) ; }" pipeline([tokenized_code]) ``` Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/single%20task/function%20documentation%20generation/java/small_model.ipynb). ## Training data The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1) ## Evaluation results For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score): Test results : | Language / Model | Python | Java | Go | Php | Ruby | JavaScript | | -------------------- | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: | | CodeTrans-ST-Small | 17.31 | 16.65 | 16.89 | 23.05 | 9.19 | 13.7 | | CodeTrans-ST-Base | 16.86 | 17.17 | 17.16 | 22.98 | 8.23 | 13.17 | | CodeTrans-TF-Small | 19.93 | 19.48 | 18.88 | 25.35 | 13.15 | 17.23 | | CodeTrans-TF-Base | 20.26 | 20.19 | 19.50 | 25.84 | 14.07 | 18.25 | | CodeTrans-TF-Large | 20.35 | 20.06 | **19.54** | 26.18 | 14.94 | **18.98** | | CodeTrans-MT-Small | 19.64 | 19.00 | 19.15 | 24.68 | 14.91 | 15.26 | | CodeTrans-MT-Base | **20.39** | 21.22 | 19.43 | **26.23** | **15.26** | 16.11 | | CodeTrans-MT-Large | 20.18 | **21.87** | 19.38 | 26.08 | 15.00 | 16.23 | | CodeTrans-MT-TF-Small | 19.77 | 20.04 | 19.36 | 25.55 | 13.70 | 17.24 | | CodeTrans-MT-TF-Base | 19.77 | 21.12 | 18.86 | 25.79 | 14.24 | 18.62 | | CodeTrans-MT-TF-Large | 18.94 | 21.42 | 18.77 | 26.20 | 14.19 | 18.83 | | State of the art | 19.06 | 17.65 | 18.07 | 25.16 | 12.16 | 14.90 | > Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)
TehranNLP/xlnet-base-cased-mnli
faa85bf85bc85d3b4762c6dc0688c679c7715a25
2021-06-03T08:36:16.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
false
TehranNLP
null
TehranNLP/xlnet-base-cased-mnli
16
null
transformers
9,199
Entry not found